types of trip generation

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3.4: Trip Generation

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Page ID 47326

David Levinson et al.
Associate Professor (Engineering) via Wikipedia

Trip Generation is the first step in the conventional four-step transportation forecasting process (followed by Destination Choice, Mode Choice, and Route Choice), widely used for forecasting travel demands. It predicts the number of trips originating in or destined for a particular traffic analysis zone.

Every trip has two ends, and we need to know where both of them are. The first part is determining how many trips originate in a zone and the second part is how many trips are destined for a zone. Because land use can be divided into two broad category (residential and non-residential) we have models that are household based and non-household based (e.g. a function of number of jobs or retail activity).

For the residential side of things, trip generation is thought of as a function of the social and economic attributes of households (households and housing units are very similar measures, but sometimes housing units have no households, and sometimes they contain multiple households, clearly housing units are easier to measure, and those are often used instead for models, it is important to be clear which assumption you are using).

At the level of the traffic analysis zone, the language is that of land uses "producing" or attracting trips, where by assumption trips are "produced" by households and "attracted" to non-households. Production and attractions differ from origins and destinations. Trips are produced by households even when they are returning home (that is, when the household is a destination). Again it is important to be clear what assumptions you are using.

People engage in activities, these activities are the "purpose" of the trip. Major activities are home, work, shop, school, eating out, socializing, recreating, and serving passengers (picking up and dropping off). There are numerous other activities that people engage on a less than daily or even weekly basis, such as going to the doctor, banking, etc. Often less frequent categories are dropped and lumped into the catchall "Other".

Every trip has two ends, an origin and a destination. Trips are categorized by purposes , the activity undertaken at a destination location.

Observed trip making from the Twin Cities (2000-2001) Travel Behavior Inventory by Gender

Some observations:

Men and women behave differently on average, splitting responsibilities within households, and engaging in different activities,
Most trips are not work trips, though work trips are important because of their peaked nature (and because they tend to be longer in both distance and travel time),
The vast majority of trips are not people going to (or from) work.

People engage in activities in sequence, and may chain their trips. In the Figure below, the trip-maker is traveling from home to work to shop to eating out and then returning home.

Specifying Models

How do we predict how many trips will be generated by a zone? The number of trips originating from or destined to a purpose in a zone are described by trip rates (a cross-classification by age or demographics is often used) or equations. First, we need to identify what we think the relevant variables are.

The total number of trips leaving or returning to homes in a zone may be described as a function of:

\[T_h = f(housing \text{ }units, household \text{ }size, age, income, accessibility, vehicle \text{ }ownership)\]

Home-End Trips are sometimes functions of:

Housing Units
Household Size
Accessibility
Vehicle Ownership
Other Home-Based Elements

At the work-end of work trips, the number of trips generated might be a function as below:

\[T_w=f(jobs(area \text{ }of \text{ } space \text{ } by \text{ } type, occupancy \text{ } rate\]

Work-End Trips are sometimes functions of:

Area of Workspace
Occupancy Rate
Other Job-Related Elements

Similarly shopping trips depend on a number of factors:

\[T_s = f(number \text{ }of \text{ }retail \text{ }workers, type \text{ }of \text{ }retail, area, location, competition)\]

Shop-End Trips are sometimes functions of:

Number of Retail Workers
Type of Retail Available
Area of Retail Available
Competition
Other Retail-Related Elements

A forecasting activity conducted by planners or economists, such as one based on the concept of economic base analysis, provides aggregate measures of population and activity growth. Land use forecasting distributes forecast changes in activities across traffic zones.

Estimating Models

Which is more accurate: the data or the average? The problem with averages (or aggregates) is that every individual’s trip-making pattern is different.

To estimate trip generation at the home end, a cross-classification model can be used. This is basically constructing a table where the rows and columns have different attributes, and each cell in the table shows a predicted number of trips, this is generally derived directly from data.

In the example cross-classification model: The dependent variable is trips per person. The independent variables are dwelling type (single or multiple family), household size (1, 2, 3, 4, or 5+ persons per household), and person age.

The figure below shows a typical example of how trips vary by age in both single-family and multi-family residence types.

The figure below shows a moving average.

Non-home-end

The trip generation rates for both “work” and “other” trip ends can be developed using Ordinary Least Squares (OLS) regression (a statistical technique for fitting curves to minimize the sum of squared errors (the difference between predicted and actual value) relating trips to employment by type and population characteristics.

The variables used in estimating trip rates for the work-end are Employment in Offices ($E_{off}$), Retail ($E_{ret}$), and Other ($E_{oth}$)

A typical form of the equation can be expressed as:

\[T_{D,k}=a_1E_{off,k}+a_2E_{oth,k}+a_3E_{ret,k}\]

$T_{D,k}$ - Person trips attracted per worker in Zone k
$E_{off,i}$ - office employment in the ith zone
$E_{oth,i}$ - other employment in the ith zone
$E_{ret,i}$- retail employment in the ith zone
$a_1,a_2,a_3$ - model coefficients

Normalization

For each trip purpose (e.g. home to work trips), the number of trips originating at home must equal the number of trips destined for work. Two distinct models may give two results. There are several techniques for dealing with this problem. One can either assume one model is correct and adjust the other, or split the difference.

It is necessary to ensure that the total number of trip origins equals the total number of trip destinations, since each trip interchange by definition must have two trip ends.

The rates developed for the home end are assumed to be most accurate,

The basic equation for normalization:

\[T'_{D,j}=T_{D,j} \dfrac{ \displaystyle \sum{i=1}^I T_{O,i}}{\displaystyle \sum{j=1}^J T_{TD,j}}\]

Sample Problems

Planners have estimated the following models for the AM Peak Hour

$T_{O,i}=1.5*H_i$

$T_{D,j}=(1.5*E_{off,j})+(1*E_{oth,j})+(0.5*E_{ret,j})$

$T_{O,i}$ = Person Trips Originating in Zone $i$

$T_{D,j}$ = Person Trips Destined for Zone $j$

$H_i$ = Number of Households in Zone $i$

You are also given the following data

A. What are the number of person trips originating in and destined for each city?

B. Normalize the number of person trips so that the number of person trip origins = the number of person trip destinations. Assume the model for person trip origins is more accurate.

Solution to Trip Generation Problem Part A

\[T'_{D,j}=T_{D,j} \dfrac{ \displaystyle \sum{i=1}^I T_{O,i}}{\displaystyle \sum{j=1}^J T_{TD,j}}=>T_{D,j} \dfrac{37500}{36750}=T_{D,j}*1.0204\]

Solution to Trip Generation Problem Part B

Modelers have estimated that the number of trips leaving Rivertown ($T_O$) is a function of the number of households (H) and the number of jobs (J), and the number of trips arriving in Marcytown ($T_D$) is also a function of the number of households and number of jobs.

$T_O=1H+0.1J;R^2=0.9$

$T_D=0.1H+1J;R^2=0.5$

Assuming all trips originate in Rivertown and are destined for Marcytown and:

Rivertown: 30000 H, 5000 J

Marcytown: 6000 H, 29000 J

Determine the number of trips originating in Rivertown and the number destined for Marcytown according to the model.

Which number of origins or destinations is more accurate? Why?

T_Rivertown =T_O ; T_O= 1(30000) + 0.1(5000) = 30500 trips

T_(MarcyTown)=T_D ; T_D= 0.1(6000) + 1(29000) = 29600 trips

Origins(T_{Rivertown}) because of the goodness of fit measure of the Statistical model (R^2=0.9).

Modelers have estimated that in the AM peak hour, the number of trip origins (T_O) is a function of the number of households (H) and the number of jobs (J), and the number of trip destinations (T_D) is also a function of the number of households and number of jobs.

$T_O=1.0H+0.1J;R^2=0.9$

Suburbia: 30000 H, 5000 J

Urbia: 6000 H, 29000 J

1) Determine the number of trips originating in and destined for Suburbia and for Urbia according to the model.

2) Does this result make sense? Normalize the result to improve its accuracy and sensibility?

$f(t_{ij})=t_{ij}^{-2}$

$T_{O,i}$ - Person trips originating in Zone i
$T_{D,j}$ - Person Trips destined for Zone j
$T_{O,i'}$ - Normalized Person trips originating in Zone i
$T_{D,j'}$ - Normalized Person Trips destined for Zone j
$T_h$ - Person trips generated at home end (typically morning origins, afternoon destinations)
$T_w$ - Person trips generated at work end (typically afternoon origins, morning destinations)
$T_s$ - Person trips generated at shop end
$H_i$ - Number of Households in Zone i
$E_{off,k}$ - office employment in Zone k
$E_{ret,k}$ - retail employment in Zone k
$E_{oth,k}$ - other employment in Zone k
$B_n$ - model coefficients

Abbreviations

H2W - Home to work
W2H - Work to home
W2O - Work to other
O2W - Other to work
H2O - Home to other
O2H - Other to home
O2O - Other to other
HBO - Home based other (includes H2O, O2H)
HBW - Home based work (H2W, W2H)
NHB - Non-home based (O2W, W2O, O2O)

External Exercises

Use the ADAM software at the STREET website and try Assignment #1 to learn how changes in analysis zone characteristics generate additional trips on the network.

Additional Problems

the start and end time (to the nearest minute)
start and end location of each trip,
primary mode you took (drive alone, car driver with passenger, car passenger, bus, LRT, walk, bike, motorcycle, taxi, Zipcar, other). (use the codes provided)
purpose (to work, return home, work related business, shopping, family/personal business, school, church, medical/dental, vacation, visit friends or relatives, other social recreational, other) (use the codes provided)
if you traveled with anyone else, and if so whether they lived in your household or not.

Bonus: Email your professor at the end of everyday with a detailed log of your travel diary. (+5 points on the first exam)

Are number of destinations always less than origins?
Pose 5 hypotheses about factors that affect work, non-work trips? How do these factors affect accuracy, and thus normalization?
What is the acceptable level of error?
Describe one variable used in trip generation and how it affects the model.
What is the basic equation for normalization?
Which of these models (home-end, work-end) are assumed to be more accurate? Why is it important to normalize trip generation models
What are the different trip purposes/types trip generation?
Why is it difficult to know who is traveling when?
What share of trips during peak afternoon peak periods are work to home (>50%, <50%?), why?
What does ORIO abbreviate?
What types of employees (ORIO) are more likely to travel from work to home in the evening peak
What does the trip rate tell us about various parts of the population?
What does the “T-statistic” value tell us about the trip rate estimation?
Why might afternoon work to home trips be more or less than morning home to work trips? Why might the percent of trips be different?
Define frequency.
Why do individuals > 65 years of age make fewer work to home trips?
Solve the following problem. You have the following trip generation model:

\[Trips=B_1Off+B_2Ind+B_3Ret\]

And you are given the following coefficients derived from a regression model.

If there are 600 office employees, 300 industrial employees, and 200 retail employees, how many trips are going from work to home?

Forecasting

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Traffic Impact Study Improvements: Part 5 – When is a Trip Not a Trip?

By Mike Spack

October 27, 2015

Guest Post by Bryant Ficek, PE, PTOE, Vice President at Spack Consulting

Earlier this year, I detailed how our standard process for a Traffic Impact Study has several points of assumptions at best or guesses at worst. This post continues that discussion. Check out the “ Top 6 Ways to Pick Apart a Traffic Study ” for more on the general topic and expect more posts to follow on this subject.

Trip generation is the process of estimating the amount of traffic a proposed development will have once it is built and operating. Trip distribution is the process by which we take the raw projected traffic for a development (trip generation) and add it to the existing volumes on the transportation network. The step in-between is determining whether all the trip generation will be new to the roadway.

To start with, there are several types of trips as follows (with definition summarized from the Institute of Transportation Engineers or ITE). The figure below illustrates the different types of trips.

Primary or New. Traffic with the specific purpose of visiting the site being studied.
Pass-By. Traffic already on the way from an origin to a primary trip destination that will make an intermediate stop at the site being studied without a route diversion.
Diverted. Traffic attracted to the site being studied from adjacent facilities without direct access to the site. A diverted trip example is a through trip on a freeway that diverts to an exit and a development, adding traffic to the local road but removing traffic from the freeway.
Internal. Traffic associated with multi-use developments where trips among various land uses can be made on the site being studied without using the major street system. These trips can be made either by walking or by vehicles using internal roadways.

These different trip generation options, combined with so many different types of land uses, leads to virtually limitless possibilities for the amount and type of traffic a particular site could generate on the roadway system. As with our trip distribution column, we initially thought about testing multiple scenarios, which would be relatively easy with today’s software. At least, theoretically. To restate our collective conclusion – While interesting on a pure research level, a thicker actual traffic impact study report covering multiple results leads us down a path no one wants to go.

Furthermore, sub-dividing the raw trip generation into parts is not something that can be quantified into a “one-size fits all” equation. Given the possibilities and the limits of our collective traffic research to date, ITE provides the best procedure to follow. So this article is dedicated to reviewing that procedure, which is spelled out in ITE’s Trip Generation Handbook and Trip Generation Manual, Volume 1 . That step-by-step process is as follows:

Raw Trip Generation. Using ITE or other land use information (try tripgeneration.org !), calculate the raw trip generation for the site.
Pass-By and Diverted Number of Trips. Use either local data or ITE data to determine a percentage of the reduced trip generation that is pass-by or diverted. Similar to the ITE Trip Generation data, both pass-by and diverted trip percentages are available by average rate or an equation for many land uses. Use this percentage to calculate the total pass-by and diverted trips for the site.
Pass-By and Diverted Trip Patterns. Use the existing traffic to determine how the pass-by and diverted trips will access the site.
Pass-By and Diverted Trip Volume Adjustment. Apply the existing traffic patterns to the pass-by and diverted trips to establish the impact on the roadway system for these trips.
Remaining Primary/New trips. Determine the remaining trip generation after reducing for internal trips and then removing the pass-by and diverted trips.
Primary/New Trip Pattern. We discussed factors to consider for the primary/new trip distribution in Part 4 of this series.
Primary/New Trip Volume Adjustment . Apply the trip distribution to the primary/new trips to determine the impact on the roadway system for these trips.
Final Volumes. Combine the pass-by, diverted, and primary/new trips at each study intersection to determine the final impact of the site being studied.

We can demonstrate this process on a theoretical study site with the following characteristics:

17,000 square feet of office, 3,000 square feet of fast food with a drive-thru, and 10 vehicle fueling positions at a gas station with convenience market
One driveway accesses the site off a busy road (1,000 vehicles in the p.m. peak hour)
A highway interchange with the busy road is located just east of the site
Trip Generation (PM Peak)
General Office, Land Use 710 – 98 raw trips
Fast Food with Drive Thru, Land Use 934 – 98 raw trips
Gas Station with Convenience Market, Land Use 945 – 136 raw trips
Internal Trips

4. Pass-By and Diverted Patterns (per the theoretical roadway data)

6. Remaining Primary/New trips:

Office – (98 raw – 5 internal – 0 pass-by – 0 diverted) = 93 primary/new trips
Fast Food – (98 raw – 21 internal – 43 pass-by – 23 diverted) = 11 primary/new trips
Gas Station – (136 raw – 22 internal – 57 pass-by – 26 diverted) = 31 primary/new trips

7. Primary/New Trip Pattern (per knowledge of theoretical area)

8. Primary/New Trip Volume Adjustment

9. Final Volumes (add the pass-by, diverted, and primary/new trips together)

We don’t include all of the above example graphs in our reports. Instead, our short-hand method is a trip generation table that looks like this:

As a final note, the internal, pass-by, diverted, and new percentages are often adjusted from the base ITE information. ITE itself notes the limited amount of data available and the inherent variability in surveyed sites. The best approach, if possible, is to discuss the percentages with the governing agency to achieve agreement and buy-in before you get too far down the path in your analysis.

Did you miss the other installments of the Traffic Impact Study Improvements series? Here are the links to the other articles:

Part 1 – Traffic Counts
Part 2 – Would Multiple Results Help Us?
Part 3 – All Trips are Equal, But Some Trips are More Equal Than Others

At what point would you apply a reduction for non-SOV trips such as transit? It looks like ITE would have you apply internal capture, transit and then pass-by.

very precious and informative post…thanks so much..I had found the answer of one of my question in another post of yours..thanks for sharing your experiences

Sometimes I drive right by a coffee shop and then think, hmm, I could really use a good cup of black coffee and then I turn around and go back. Is this a pass-by trip or a diverted trip (or both?). What about a cappuccino?

If the need for java hit as you approached the site, and you turned right in, this would be a standard diversion. But since you passed the driveway and turned around, ….you need to take public transit and stop burning up our resources.

Hey Mike, After you do trip gen. calcs (let’s say for a built-up year 2029) but you want to consider design year (typical 20 years) and you want to design/improve nearby roads for the design year 2049 (2029 + 20 years); do you apply same growth rate on trip generated as you would do for existing traffic? or you apply the growth rate on existing traffic, and use trip gen numbers without applying a growth rate?

No – the growth rate is not applied to the trip generation. For instance, the trips generated by a single family home isn’t going to keep growing over time. They’re assumed to be static.

My mission is to help traffic engineers, transportation planners, and other transportation professionals improve our world.

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Trip generation

What is trip generation .

A trip is usually defined in transport modeling as a single journey made by an individual between two points by a specified mode of travel and for a defined purpose. Trips are often considered as productions of a particular land-use and attracted to other specified land-uses. The number of trips arises in unit time, usually for a specified zonal land use , is called the trip generation rate.

How to estimate trip generation ?

Trip generation is estimated in three ways:

(i) traditionally by linear and multiple regression

(ii) by aggregating the trip generating capability of a household or car and aggregating the total according to the distribution of each selected category in the zones, and

(iii) by household classification method through a catalogue of the characteristic mean trip rates for specific types of household.

The attraction points are identified as trip generated by work, and other purpose visits. By assigning suitable values to the independent variables of the regression equations forecasts can be made of the future trip ends for zones by either method.

Trip distribution :Trip generation estimates the number and types of trips originating and terminating in zones. Trip distribution is the process of computing the number of trips between one zone and all other. A trip matrix is drawn up with the sums of rows indicating the total number of trips originating in zone i and the sums of columns the total number of destinations attracted to zone j.

Each cell in the matrix indicates the number of trips that go from each origin zone to each destination zone. The trips on the diagonal are intra-zonal trips, trips that originate and end in the same zone. The balancing equation is implemented in a series of steps that include modeling the number of trips originating in each cases, adding in trips originating from outside the study area(external trips), and statistically balancing the origins and destinations.

This is done in the trip generation stage. But, it is essential that the step should have been completed for the trip distribution to be implemented. Two trip distribution matrices need to be distinguished. The first is the observed distribution. This is the actual number of trips that are observed traveling between each origin zone and each destination zone. It is calculated by simply enumerating the number of trips by each origin-destination combination. It is also called trip-link. The second distribution is a model of the trip distribution matrix, called the predicted distribution.

Generally trips should be distributed over the area proportionally to the attractiveness of activities and inversely proportional to the travel resistances between areas. It is assumed that the trips between zones will be by the most direct or cheapest routes and, taking each zone in turn, a minimum path is traced out to all other zones to form a minimum path tree. The trip distribution is a model of travel between zones-trips or links. The modeled trip distribution can then be compared to the actual distribution to see whether the model produces a reasonable approximation.

Read about: Zoning of Land for OD Survey , Traffic Volume Count , Origin Destination Survey Methods

About The Author

Fundamentals of Transportation/Trip Generation

Trip Generation is the first step in the conventional four-step transportation forecasting process (followed by Destination Choice , Mode Choice , and Route Choice ), widely used for forecasting travel demands. It predicts the number of trips originating in or destined for a particular traffic analysis zone.

1 Activities
2.1 Home-end
2.2 Work-end
2.3 Shop-end
3 Input Data
4.1 Home-end
4.2 Non-home-end
5 Normalization
6 Sample Problems
7 Variables
8 Abbreviations
9 External Exercises
10 Additional Problems
11 End Notes
12 Further reading
14 References

Activities [ edit | edit source ]

Every trip has two ends, an origin and a destination. Trips are categorized by purposes , the activity undertaken at a destination location.

Some observations:

Men and women behave differently on average, splitting responsibilities within households, and engaging in different activities,
Most trips are not work trips, though work trips are important because of their peaked nature (and because they tend to be longer in both distance and travel time),
The vast majority of trips are not people going to (or from) work.

People engage in activities in sequence, and may chain their trips. In the Figure below, the trip-maker is traveling from home to work to shop to eating out and then returning home.

Specifying Models [ edit | edit source ]

Home-end [ edit | edit source ]

The total number of trips leaving or returning to homes in a zone may be described as a function of:

$T_{h}=f(housing\ units,\ household\ size,\ age,\ income,\ accessibility,\ vehicle\ ownership).\,\!$

Home-End Trips are sometimes functions of:

Housing Units
Household Size
Accessibility
Vehicle Ownership
Other Home-Based Elements

Work-end [ edit | edit source ]

At the work-end of work trips, the number of trips generated might be a function as below:

$T_{w}=f(jobs(area\ of\ space\ by\ type,\ occupancy\ rate))\,\!$

Work-End Trips are sometimes functions of:

Area of Workspace
Occupancy Rate
Other Job-Related Elements

Shop-end [ edit | edit source ]

Similarly shopping trips depend on a number of factors:

$\,\!T_{s}=f(number\ of\ retail\ workers,\ type\ of\ retail,\ area,\ location,\ competition)$

Shop-End Trips are sometimes functions of:

Number of Retail Workers
Type of Retail Available
Area of Retail Available
Competition
Other Retail-Related Elements

Input Data [ edit | edit source ]

Estimating Models [ edit | edit source ]

Which is more accurate: the data or the average? The problem with averages (or aggregates) is that every individual’s trip-making pattern is different.

The figure below shows a typical example of how trips vary by age in both single-family and multi-family residence types.

The figure below shows a moving average.

Non-home-end [ edit | edit source ]

$E_{off}\,\!$

A typical form of the equation can be expressed as:

$T_{D,k}=a_{1}E_{off,k}+a_{2}E_{oth,k}+a_{3}E_{ret,k}\,\!$

Normalization [ edit | edit source ]

It is necessary to ensure that the total number of trip origins equals the total number of trip destinations, since each trip interchange by definition must have two trip ends.

The rates developed for the home end are assumed to be most accurate,

The basic equation for normalization:

$T'_{D,j}=T_{D,j}{\frac {\sum \limits _{i=1}^{I}{T_{O,i}}}{\sum \limits _{j=1}^{J}{T_{D,j}}}}\,\!$

Sample Problems [ edit | edit source ]

Problem ( Solution )

Variables [ edit | edit source ]

$T_{O},i$

Abbreviations [ edit | edit source ]

H2W - Home to work
W2H - Work to home
W2O - Work to other
O2W - Other to work
H2O - Home to other
O2H - Other to home
O2O - Other to other
HBO - Home based other (includes H2O, O2H)
HBW - Home based work (H2W, W2H)
NHB - Non-home based (O2W, W2O, O2O)

External Exercises [ edit | edit source ]

Use the ADAM software at the STREET website and try Assignment #1 to learn how changes in analysis zone characteristics generate additional trips on the network.

Additional Problems [ edit | edit source ]

Additional Problems

End Notes [ edit | edit source ]

Videos [ edit | edit source ]

Trip Generation
Normalization

References [ edit | edit source ]

Book:Fundamentals of Transportation

Trip Generation Manual, 11th Edition

This new edition of the Trip Generation Manual enhances the 10th edition’s modernized content, data set, and contemporary delivery - making it an invaluable resource. The 11th edition features: (1) All the latest multimodal trip generation data for urban, suburban and rural applications, (2) Reclassified land uses to better meet user needs, (3) Integrated digital copies of all land use definitions, plots and supporting materials, and (4) Full ability to filter the data to match local conditions (in digital versions only).

Record URL: https://ecommerce.ite.org/IMIS/ItemDetail?iProductCode=TG11-C
Record URL: https://ecommerce.ite.org/IMIS/ItemDetail?iProductCode=TG11-D
Record URL: https://ecommerce.ite.org/IMIS/ItemDetail?iProductCode=IR-016L
Summary URL: https://www.ite.org/technical-resources/topics/trip-and-parking-generation/
Find a library where document is available. Order URL: http://worldcat.org/isbn/9781734507874
Abstract reprinted with permission from the Institute of Transportation Engineers.

Institute of Transportation Engineers (ITE)

Publication Date: 2021-9
Media Type: Digital/other

Subject/Index Terms

TRT Terms: Land use ; Multimodal transportation ; Trip generation
Subject Areas: Highways; Planning and Forecasting;

Filing Info

Accession Number: 01787570
Record Type: Publication
ISBN: 9781734507874
Files: TRIS
Created Date: Nov 10 2021 1:54PM

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Part III: Travel Demand Modeling

9 Introduction to Transportation Modeling: Travel Demand Modeling and Data Collection

Chapter Overview

Chapter 9 serves as an introduction to travel demand modeling, a crucial aspect of transportation planning and policy analysis. As explained in previous chapters, the spatial distribution of activities such as employment centers, residential areas, and transportation systems mutually influence each other. The utilization of travel demand forecasting techniques leads to dynamic processes in urban areas. A comprehensive grasp of travel demand modeling is imperative for individuals involved in transportation planning and implementation.

This chapter covers the fundamentals of the traditional four-step travel demand modeling approach. It delves into the necessary procedures for applying the model, including establishing goals and criteria, defining scenarios, developing alternatives, collecting data, and conducting forecasting and evaluation.

Following this chapter, each of the four steps will be discussed in detail in Chapters 10 through 13.

Learning Objectives

Student Learning Outcomes

Describe the need for travel demand modeling in urban transportation and relate it to the structure of the four-step model (FSM).
Summarize each step of FSM and the prerequisites for each in terms of data requirement and model calibration.
Summarize the available methods for each of the first three steps of FSM and compare their reliability.
Identify assumptions and limitations of each of the four steps and ways to improve the model.

Introduction

Transportation planning and policy analysis heavily rely on travel demand modeling to assess different policy scenarios and inform decision-making processes. Throughout our discussion, we have primarily explored the connection between urban activities, represented as land uses, and travel demands, represented by improvements and interventions in transportation infrastructure. Figure 9.1 provides a humorous yet insightful depiction of the transportation modeling process. In preceding chapters, we have delved into the relationship between land use and transportation systems, with the houses and factories in the figure symbolizing two crucial inputs into the transportation model: households and jobs. The output of this model comprises transportation plans, encompassing infrastructure enhancements and programs. Chapter 9 delves into a specific model—travel demand modeling. For further insights into transportation planning and programming, readers are encouraged to consult the UTA OERtransport book, “Transportation Planning, Policies, and History.”

A graphical representation of FSM input and outputs data in the process.

Travel demand models forecast how people will travel by processing thousands of individual travel decisions. These decisions are influenced by various factors, including living arrangements, the characteristics of the individual making the trip, available destination options, and choices regarding route and mode of transportation. Mathematical relationships are used to represent human behavior in these decisions based on existing data.

Through a sequential process, transportation modeling provides forecasts to address questions such as:

What will the future of the area look like?
What is the estimated population for the forecasting year?
How are job opportunities distributed by type and category?
What are the anticipated travel patterns in the future?
How many trips will people make? ( Trip Generation )
Where will these trips end? ( Trip Distribution )
Which transportation mode will be utilized? ( Mode Split )
What will be the demand for different corridors, highways, and streets? ( Traffic Assignment )
Lastly, what impact will this modeled travel demand have on our area? (Rahman, 2008).

9.2 Four-step Model

According to the questions above, Transportation modeling consists of two main stages, regarding the questions outlined above. Firstly, addressing the initial four questions involves demographic and land use analysis, which incorporates the community vision collected through citizen engagement and input. Secondly, the process moves on to the four-step travel demand modeling (FSM), which addresses questions 5 through 8. While FSM is generally accurate for aggregate calculations, it may occasionally falter in providing a reliable test for policy scenarios. The limitations of this model will be explored further in this chapter.

In the first stage, we develop an understanding of the study area from demographic information and urban form (land-use distribution pattern). These are important for all the reasons we discussed in this book. For instance, we must obtain the current age structure of the study area, based on which we can forecast future birth rates, death, and migrations (Beimborn & Kennedy, 1996).

Regarding economic forecasts, we must identify existing and future employment centers since they are the basis of work travel, shopping travel, or other travel purposes. Empirically speaking, employment often grows as the population grows, and the migration rate also depends on a region’s economic growth. A region should be able to generate new employment while sustaining the existing ones based upon past trends and form the basis for judgment for future trends (Mladenovic & Trifunovic, 2014).

After forecasting future population and employment, we must predict where people go (work, shop, school, or other locations). Land-use maps and plans are used in this stage to identify the activity concentrations in the study area. Future urban growth and land use can follow the same trend or change due to several factors, such as the availability of open land for development and local plans and zoning ordinances (Beimborn & Kennedy, 1996). Figure 9.3 shows different possible land-use patterns frequently seen in American cities.

This pictures shows 6 different land use patterns that are: (a) traditional grid, (b) post-war suburb, (c) traditional neighborhood design, (d) fused grid, (e) post-war suburb II, and (f) tranditional neighborhood design II.

Land-use pattern can also be forecasted through the integration of land use and transportation as we explored in previous chapters.

Figure 9.3 above shows a simple structure of the second stage of FSM.

This picture shows the sequence of the fours steps of FSM.

Once the number and types of trips are predicted, they are assigned to various destinations and modes. In the final step, these trips are allocated to the transportation network to compute the total demand for each road segment. During this second stage, additional choices such as the time of travel and whether to travel at all can be modeled using choice models (McNally, 2007). Travel forecasting involves simulating human behavior through mathematical series and calculations, capturing the sequence of decisions individuals make within an urban environment.

The first attempt at this type of analysis in the U.S. occurred during the post-war development period, driven by rapid economic growth. The influential study by Mitchell and Rapkin (1954) emphasized the need to establish a connection between travel and activities, highlighting the necessity for a comprehensive framework. Initial development models for trip generation, distribution, and diversion emerged in the 1950s, leading to the application of the four-step travel demand modeling (FSM) approach in a transportation study in the Chicago area. This model was primarily highway-oriented, aiming to compare new facility development and improved traffic engineering. In the 1960s, federal legislation mandated comprehensive and continuous transportation planning, formalizing the use of FSM. During the 1970s, scholars recognized the need to revise the model to address emerging concerns such as environmental issues and the rise of multimodal transportation systems. Consequently, enhancements were made, leading to the development of disaggregate travel demand forecasting and equilibrium assignment methods that complemented FSM. Today, FSM has been instrumental in forecasting travel demand for over 50 years (McNally, 2007; Weiner, 1997).

Initially outlined by Mannheim (1979), the basic structure of FSM was later expanded by Florian, Gaudry, and Lardinois (1988). Figure 9.3 illustrates various influential components of travel demand modeling. In this representation, “T” represents transportation, encompassing all elements related to the transportation system and its services. “A” denotes the activity system, defined according to land-use patterns and socio-demographic conditions. “P” refers to transportation network performance. “D,” which stands for demand, is generated based on the land-use pattern. According to Florian, Gaudry, and Lardinois (1988), “L” and “S” (location and supply procedures) are optional parts of FSM and are rarely integrated into the model.

This flowchart shows the relationship between various components of transportation network and their joint impact on traffic volume (flow) on the network.

A crucial aspect of the process involves understanding the input units, which are defined both spatially and temporally. Demand generates person trips, which encompass both time and space (e.g., person trips per household or peak-hour person trips per zone). Performance typically yields a level of service, defined as a link volume capacity ratio (e.g., freeway vehicle trips per hour or boardings per hour for a specific transit route segment). Demand is primarily defined at the zonal level, whereas performance is evaluated at the link level.

It is essential to recognize that travel forecasting models like FSM are continuous processes. Model generation takes time, and changes may occur in the study area during the analysis period.

Before proceeding with the four steps of FSM, defining the study area is crucial. Like most models discussed, FSM uses traffic analysis zones (TAZs) as the geographic unit of analysis. However, a higher number of TAZs generally yield more accurate results. The number of TAZs in the model can vary based on its purpose, data availability, and vintage. These zones are characterized or categorized by factors such as population and employment. For modeling simplicity, FSM assumes that trip-making begins at the center of a zone (zone centroid) and excludes very short trips that start and end within a TAZ, such as those made by bike or on foot.

Furthermore, highway systems and transit systems are considered as networks in the model. Highway or transit line segments are coded as links, while intersections are represented as nodes. Data regarding network conditions, including travel times, speeds, capacity, and directions, are utilized in the travel simulation process. Trips originate from trip generation zones, traverse a network of links and nodes, and conclude at trip attraction zones.

Trip Generation

Trip generation is the first step in the FSM model. This step defines the magnitude of daily travel in the study area for different trip purposes. It will also provide us with an estimate of the total trips to and from each zone, creating a trip production and attraction matrix for each trip’s purpose. Trip purposes are typically categorized as follows:

Home-based work trips (work trips that begin or end at home),
Home-based shopping trips,
Home-based other trips,
School trips,
Non-home-based trips (trips that neitherbeginnorendathome),
Trucktrips,and
Taxitrips(Ahmed,2012).

Trip attractions are based on the level of employment in a zone. In the trip generation step, the assumptions and limitations are listed below:

Independent decisions: Travel behavior is affected by many factors generated within a household; the model ignores most of these factors. For example, childcare may force people to change their travel plans.
Limited trip purposes: This model consists of a limited number of trip purposes for simplicity, giving rise to some model limitations. Take shopping trips, for example; they are all considered in the same weather conditions. Similarly, we generate home-based trips for various purposes (banking, visiting friends, medical reasons, or other purposes), all of which are affected by factors ignored by the model.
Trip combinations: Travelers are often willing to combine various trips into a chain of short trips. While this behavior creates a complex process, the FSM model treats this complexity in a limited way.
Feedback, cause, and effect problems: Trip generation often uses factors that are a function of the number of trips. For instance, for shopping trip attractions in the FSM model, we assume they are a retail employment function. However, it is logical to assume how many customers these retail centers attract. Alternatively, we can assume that the number of trips a household makes is affected by the number of private cars they own. Nevertheless, the activity levels of families determine the total number of cars.

As mentioned, trip generation process estimations are done separately for each trip purpose. Equations 1 and 2 show the function of trip generation and attraction:

$O_i = f(x_{i1}, x_{i2}, x_{i3}, \ldots)$

where Oi and Dj trip are generated and attracted respectively, x refers to socio-economic characteristics, and y refers to land-use properties.

Generally, FSM aggregates different trip purposes previously listed into three categories: home-based work trips (HBW) , home-based other (or non-work) trips (HBO) , and non-home-based trips (NHB) . Trip ends are either the origin (generation) or destination (attraction), and home-end trips comprise most trips in a study area. We can also model trips at different levels, such as zones, households, or person levels (activity-based models). Household-level models are the most common scale for trip productions, and zonal-level models are appropriate for trip attractions (McNally, 2007).

There are three main methods for a trip generation or attraction.

The first method is multiple regression based on population, jobs, and income variables.
The second method in this step is experience-based analysis, which can show us the ratio of trips generated frequently.
The third method is cross-classification . Cross-classification is like the experience-based analysis in that it uses trip rates but in an extended format for different categories of trips (home-based trips or non-home-based trips) and different attributes of households, such as car ownership or income.

Elaborating on the differences between these methods, category analysis models are more common for the trip generation model, while regression models demonstrate better performance for trip attractions (Meyer, 2016). Production models are recognized to be influenced by a range of explanatory and policy-sensitive variables (e.g., car ownership, household income, household size, and the number of workers). However, estimation is more problematic for attraction models because regional travel surveys are at the household level (thus providing more accurate data for production models) and not for nonresidential land uses (which is important for trip attraction). Additionally, estimation can be problematic because explanatory trip attraction variables may usually underperform (McNally, 2007). For these reasons, survey data factoring is required prior to relating sample trips to population-level attraction variables, typically achieved via regression analysis. Table 9.1 shows the advantages and disadvantages of each of these two models.

Note. Table adapted from “The Four-Step Model” by M. McNally, In D. A. Hensher, & K. J. Button (Eds.), Handbook of transport modelling , Volume1, p. 5, Bingley, UK: Emerald Publishing. Copyright 2007 by Emerald Publishing.

Trip Distribution

Thus far, the number of trips beginning or ending in a particular zone have been calculated. The second step explores how trips are distributed between zones and how many trips are exchanged between two zones. Imagine a shopping trip. There are multiple options for accessible shopping malls accessible. However, in the end, only one will be selected for the destination. This information is modeled in the second step as a distribution of trips. The second step results are usually a very large Origin-Destination (O-D) matrix for each trip purpose. The O-D matrix can look like the table below (9.2), in which sum of Tij by j shows us the total number of trips attracted in zone J and the sum of Tij by I yield the total number of trips produced in zone I.

Up to this point, we have calculated the number of trips originating from or terminating in a specific zone. The next step involves examining how these trips are distributed across different zones and how many trips are exchanged between pairs of zones. To illustrate, consider a shopping trip: there are various options for reaching shopping malls, but ultimately, only one option is chosen as the destination. This process is modeled in the second step as the distribution of trips. The outcome of this step typically yields a large Origin-Destination (O-D) matrix for each trip purpose. An O-D matrix might resemble the table below (9.2), where the sum of Tij by j indicates the total number of trips attracted to zone J, and the sum of Tij by I represents the total number of trips originating from zone I.

The gravity model developed by Hansen (1959), elaborated in previous chapters, will be used to calculate the trip distribution. This spatial interaction model considers the trip produced and attracted for each zone as a contributing factor and the distance between them as an impedance function (Rodrigue, 2020). Recall from previous chapters that the gravity model formula is as follows:

$T_{ij} = \frac{P(A_i F_{ij}(K_{ij}))}{\sum(A_x F_{ij}(k_{ix}))}$

T ij = trips produced at I and attracted at j

P i = total trip production at I

A j = total trip attraction at j

F ij = a calibration term for interchange ij , (friction factor) or travel

time factor ( F ij =C/t ij n )

C= calibration factor for the friction factor

K ij = a socioeconomic adjustment factor for interchange ij

i = origin zone

n = number of zones

Different methods (units) in the gravity model can be used to perform distance measurements. For instance, distance can be represented by time, network distance, or travel costs. For travel costs, auto travel cost is the most common and straightforward way of monetizing distance. A combination of different costs, such as travel time, toll payments, parking payments, etc., can also be used. Alternatively, a composite cost of both car and transit costs can be used (McNally, 2007).

Generalized travel costs can be a function of time divided into different segments. For instance, public transit time can be divided into the following segments: in-vehicle time, walking time, waiting time, interchange time, fare, etc. Since travelers perceive time value differently for each segment (like in-vehicle time vs. waiting time), weights are assigned based on the perceived value of time (VOT). Similarly, car travel costs can be categorized into in-vehicle travel time or distance, parking charge, tolls, etc.

As with the first step in the FSM model, the second step has assumptions and limitations that are briefly explained below.

Constant trip times: In order to utilize the model for prediction, it assumes that the duration of trips remains constant. This means that travel distances are measured by travel time, and the assumption is that enhancements in the transportation system, which reduce travel times, are counterbalanced by the separation of origins and destinations.
Automobile travel times to represent distance: We utilize travel time as a proxy for travel distance. In the gravity model, this primarily relies on private car travel time and excludes travel times via other modes like public transit. This leads to a broader distribution of trips.
Limited consideration of socio-economic and cultural factors: Another drawback of the gravity model is its neglect of certain socio-economic or cultural factors. Essentially, this model relies on trip production and attraction rates along with travel times between them for predictions. Consequently, it may overestimate trip rates between high-income groups and nearby low-income Traffic Analysis Zones (TAZs). Therefore, incorporating more socio-economic factors into the model would enhance accuracy.
Feedback issues: The gravity model’s reliance on travel times is heavily influenced by congestion levels on roads. However, measuring congestion proves challenging, as discussed in subsequent sections. Typically, travel times are initially assumed and later verified. If the assumed values deviate from actual values, they require adjustment, and the calculations need to be rerun.

Mode choice

FSM model’s third step is a mode-choice estimation that helps identify what types of transportation travelers use for different trip purposes to offer information about users’ travel behavior. This usually results in generating the share of each transportation mode (in percentages) from the total number of trips in a study area using the utility function (Ahmed, 2012). Performing mode-choice estimations is crucial as it determines the relative attractiveness and usage of various transportation modes, such as public transit, carpooling, or private cars. Modal split analysis helps evaluate improvement programs or proposals (e.g., congestion pricing or parking charges) aimed at enhancing accessibility or service levels. It is essential to identify the factors contributing to the utility and disutility of different modes for different travel demands (Beimborn & Kennedy, 1996). Comparing the disutility of different modes between two points aids in determining mode share. Disutility typically refers to the burdens of making a trip, such as time, costs (fuel, parking, tolls, etc.). Once disutility is modeled for different trip purposes between two points, trips can be assigned to various modes based on their utility. As discussed in Chapter 12, a mode’s advantage in terms of utility over another can result in a higher share of trips using that mode.

The assumptions and limitations for this step are outlined as follows:

Choices are only affected by travel time and cost: This model assumes that changes in mode choices occur solely if transportation cost or travel time in the transportation network or transit system is altered. For instance, a more convenient transit mode with the same travel time and cost does not affect the model’s results.
Omitted factors: Certain factors like crime, safety, and security, which are not included in the model, are assumed to have no effect, despite being considered in the calibration process. However, modes with different attributes regarding these omitted factors yield no difference in the results.
Simplified access times: The model typically overlooks factors related to the quality of access, such as neighborhood safety, walkability, and weather conditions. Consequently, considerations like walkability and the impact of a bike-sharing program on the attractiveness of different modes are not factored into the model.
Constant weights: The model assumes that the significance of travel time and cost remains constant for all trip purposes. However, given the diverse nature of trip purposes, travelers may prioritize travel time and cost differently depending on the purpose of their trip.

The most common framework for mode choice models is the nested logit model, which can accommodate various explanatory variables. However, before the final step, results need to be aggregated for each zone (Koppelman & Bhat, 2006).

A generalized modal split chart is depicted in Figure 9.5.

a simple decision tree for transportation mode choice between car, train, and walking.

In our analysis, we can use binary logit models (dummy variable for dependent variable) if we have two modes of transportation (like private cars and public transit only). A binary logit model in the FSM model shows us if changes in travel costs would occur, such as what portion of trips changes by a specific mode of transport. The mathematical form of this model is:

$P_ij^1=\frac{T_ij^1}{T_{ij}}\ =\frac{e^-bcij^1 }{e^(-bc_ij^1 )+e^(-bc_ij^2 )}$

where: P_ij 1= The proportion of trips between i and j by mode 1 . Tij 1= Trips between i and j by mode 1.

Cij 1= Generalized cost of travel between i and j by mode 1 .

Cij^2= Generalized cost of travel between i and j by mode 2 .

b= Dispersion Parameter measuring sensitivity to cost.

It is also possible to have a hierarchy of transportation modes for using a binary logit model. For instance, we can first conduct the analysis for the private car and public transit and then use the result of public transit to conduct a binary analysis between rail and bus.

Trip assignment

After breaking down trip counts by mode of transportation, we analyze the routes commuters take from their starting point to their destination, especially for private car trips. This process is known as trip assignment and is the most intricate stage within the FSM model. Initially, the minimum path assigns trips for each origin-destination pair based on either travel costs or time. Subsequently, the assigned volume of trips is compared to the capacity of the route to determine if congestion would occur. If congestion does happen (meaning that traffic volume exceeds capacity), the speed of the route needs to be decreased, resulting in increased travel costs or time. When the Volume/Capacity ratio (v/c ratio) changes due to congestion, it can lead to alterations in both speed and the shortest path. This characteristic of the model necessitates an iterative process until equilibrium is achieved.

The process for public transit is similar, but with one distinction: instead of adjusting travel times, headways are adjusted. Headway refers to the time between successive arrivals of a vehicle at a stop. The duration of headways directly impacts the capacity and volume for each transit vehicle. Understanding the concept of equilibrium in the trip assignment step is crucial because it guides the iterative process of the model. The conclusion of this process is marked by equilibrium, a concept known as Wardrop equilibrium. In Wardrop equilibrium, traffic naturally organizes itself in congested networks so that individual commuters do not switch routes to reduce travel time or costs. Additionally, another crucial factor in this step is the time of day.

Like previous steps, the following assumptions and limitations are pertinent to the trip assignment step:

1. Delays on links: Most traffic assignment models assume that delays occur on the links, not the intersections. For highways with extensive intersections, this can be problematic because intersections involve highly complex movements. Intersections are excessively simplified if the assignment process does not modify control systems to reach an equilibrium.

2. Points and links are only for trips: This model assumes that all trips begin and finish at a single point in a zone (centroids), and commuters only use the links considered in the model network. However, these points and links can vary in the real world, and other arterials or streets might be used for commutes.

3. Roadway capacities: In this model, a simple assumption helps determine roadways’ capacity. Capacity is found based on the number of lanes a roadway provides and the type of road (highway or arterial).

4. Time of the day variations: Traffic volume varies greatly throughout the day and week. In this model, a typical workday of the week is considered and converted to peak hour conditions. A factor used for this step is called the hour adjustment factor. This value is critical because a small number can result in a massive difference in the congestion level forecasted on the model.

5. Emphasis on peak hour travel: The model forecasts for the peak hour but does not forecast for the rest of the day. The models make forecasts for a typical weekday but neglect specific conditions of that time of the year. After completing the fourth step, precise approximations of travel demand or traffic count on each road are achieved. Further models can be used to simulate transportation’s negative or positive externalities. These externalities include air pollution, updated travel times, delays, congestion, car accidents, toll revenues, etc. These need independent models such as emission rate models (Beimborn & Kennedy, 1996).

The basic equilibrium condition point calculation is an algorithm that involves the computation of minimum paths using an all-or-nothing (AON) assignment model to these paths. However, to reach equilibrium, multiple iterations are needed. In AON, it is assumed that the network is empty, and a free flow is possible. The first iteration of the AON assignment requires loading the traffic by finding the shortest path. Due to congestion and delayed travel times, the

previous shortest paths may no longer be the best minimum path for a pair of O-D. If we observe a notable decrease in travel time or cost in subsequent iterations, then it means the equilibrium point has not been reached, and we must continue the estimation. Typically, the following factors affect private car travel times: distance, free flow speed on links, link capacity, link speed capacity, and speed flow relationship .

The relationship between the traffic flow and travel time equation used in the fourth step is:

$t = t_0 + a v^n, \quad v < c$

t= link travel time per length unit

t 0 =free-flow travel time

v=link flow

c=link capacity

a, b, and n are model (calibrated) parameters

Model improvement

Improvements to FSM continue to generate more accurate results. Since transportation dynamics in urban and regional areas are under the complex influence of various factors, the existing models may not be able to incorporate all of them. These can be employer-based trip reduction programs, walking and biking improvement schemes, a shift in departure (time of the day), or more detailed information on socio-demographic and land-use-related factors. However, incorporating some of these variables is difficult and can require minor or even significant modifications to the model and/or computational capacities or software improvements. The following section identifies some areas believed to improve the FSM model performance and accuracy.

• Better data: An effective way of improving the model accuracy is to gather a complete dataset that represents the general characteristics of the population and travel pattern. If the data is out- of-date or incomplete, we will get poor results.

• Better modal split: As you saw in previous sections, the only modes incorporated into the model are private car and public transit trips, while in some cities, a considerable fraction of trips are made by bicycle or by walking. We can improve our models by producing methods to consider these trips in the first and third steps.

• Auto occupancy: In contemporary transportation planning practices, especially in the US, some new policies are emerging for carpooling. We can calculate auto occupancy rates using different mode types, such as carpooling, sensitive to private car trips’ disutility, parking costs, or introducing a new HOV lane.

• Time of the day: In this chapter, the FSM framework discussed is oriented toward peak hour (single time of the day) travel patterns. Nonetheless, understanding the nature of congestion in other hours of the day is also helpful for understanding how travelers choose their travel time.

• A broader trip purpose: Additional trip purposes may provide a better understanding of the

factors affecting different trip purposes and trip-chaining behaviors. We can improve accuracy by having more trip purposes (more disaggregate input and output for the model).

The concept of access: As discussed, land-use policies that encourage public transit use or create amenities for more convenient walking are not present in the model. Developing factors or indices that reflect such improvements in areas with high demand for non-private vehicles and incorporating them in choice models can be a good improvement.
Land use feedback: To better understand interactions between land use and travel demand, a land-use simulation model can be added to these steps to determine how a proposed transportation change will lead to a change in land use.
Intersection delays: As mentioned in the fourth step, intersections in major highways create significant delays. Incorporating models that calculate delays at these intersections, such as stop signs, could be another improvement to the model.

A Simple Example of the FSM model

An example of FSM is provided in this section to illustrate a typical application of this model in the U.S. In the first phase, the specifications about the transportation network and household data are needed. In this hypothetical example, 5 percent of households in each TAZ were sampled and surveyed, which generated 1,955 trips in 200 households. As a hypothetical case study, this sample falls below the standard required for statistical significance but is relevant to demonstrate FSM.

A home interview survey was carried out to gather data from a five percent sample of households in each TAZ. This survey resulted in 1,852 trips from 200 households. It is important to note that the sample size in this example falls below the minimum required for statistical significance, as it is intended for learning purposes only.

Table 9.3 provides network information such as speed limits, number of lanes, and capacity. Table 9.4 displays the total number of households and jobs in three industry sectors for each zone. Additionally, Table 9.5 breaks down the household data into three car ownership groups, which is one of the most significant factors influencing trip making.

In the first step (trip generation), a category model (i.e., cross-classification) helped estimate trips. The sampled population’s sociodemographic and trip data for different purposes helped calculate this estimate. Since research has shown the significant effect of auto ownership on private car trip- making (Ben-Akiva & Lerman, 1974), disaggregating the population based on the number of private cars generates accurate results. Table 9.7 shows the trip-making rate for different income and auto ownership groups.

Also, as mentioned in previous sections, multiple regression estimation analysis can be used to generate the results for the attraction model. Table 9.7 shows the equations for each of the trip purposes.

After estimating production and attraction, the models are used for population data to generate results for the first step. Next, comparing the results of trip production and attraction, we can observe that the total number of trips for each purpose is different. This can be due to using different methods for production and attraction. Since the production method is more reliable, attraction is typically normalized by production. Also, some external zones in our study area are either attracting trips from our zones or generating them. In this case, another alternative is to extend the boundary of the study area and include more zones.

As mentioned, the total number of trips produced and attracted are different in these results. To address this mismatch, we can use a balance factor to come up with the same trip generation and attraction numbers if we want to keep the number of zones within our study area. Alternatively, we can consider some external stations in addition to designated zones. In this example, using the latter seems more rational because, as we saw in Table 9.4, there are more jobs than the number of households aggregately, and our zone may attract trips from external locations.

For the trip distribution step, we use the gravity model. For internal trips, the gravity model is:

$T_{ij} = a_i b_j P_i A_j f(t_{ij})$

and f(tij) is some function of network level of service (LOS)

To apply the gravity model, we need to calculate the impedance function first, which is represented here by travel cost. Table 9.9 shows the minimum travel path between each pair of zones ( skim tree ) in a matrix format in which each cell represents travel time required to travel between the corresponding row and column of that cell.

Table 9.9-Travel cost table (skim tree)

With having minimum travel costs between each pair of zones, we can calculate the impedance function for each trip purpose using the formula

$f(t_{ij}) = a \cdot t_{ij} \cdot b \cdot e^{ct_{ij}}$

Table 9.10 shows the model parameters for calculating the impedance function for different trip purposes:

After calculating the impedance function , we can calculate the result of the trip distribution. This stage generates trip matrices since we calculate trips between each zone pair. These matrices are usually in “Origin-Destination” (OD) format and can be disaggregated by the time of day. Field surveys help us develop a base-year trip distribution for different periods and trip purposes. Later, these empirical results will help forecast trip distribution. When processing the surveys, the proportion of trips from the production zone to the attraction zone (P-A) is also generated. This example can be seen in Table 9.11. Looking at a specific example, the first row in table is for the 2-hour morning peak commute time period. The table documents that the production to attraction factor for the home-based work trip is 0.3. Unsurprisingly, the opposite direction, attraction to production zone is 0.0 for this time of day. Additionally, the table shows that the factor for HBO and NHB trips are low but do occur during this time period. This could represent shopping trips or trips to school. Table 9.11 table also contains the information for average occupancy levels of vehicles from surveys. This information can be used to convert person trips to vehicle trips or vice versa.

Table 9.11 Trip distribution rates for different time of the day and trip purposes

The O-D trip table is calculated by adding the multiplication of the P-to-A factor by corresponding cell of the P-A trip table and adding the corresponding cell of the transposed P-A trip table multiplied by the A-to-P factor. These results, which are the final output of second step, are shown in Table 9.12.

Once the Production-Attraction (P-A) table is transformed into Origin-Destination (O-D) format and the complete O-D matrix is computed, the outcomes will be aggregated for mode choice and traffic assignment modeling. Further elaboration on these two steps will be provided in Chapters 11 and 12.

In this chapter, we provided a comprehensive yet concise overview of four-step travel demand modeling including the process, the interrelationships and input data, modeling part and extraction of outputs. The complex nature of cities and regions in terms of travel behavior, the connection to the built environment and constantly growing nature of urban landscape, necessitate building models that are able to forecast travel patterns for better anticipate and prepare for future conditions from multiple perspectives such as environmental preservation, equitable distribution of benefits, safety, or efficiency planning. As we explored in this book, nearly all the land-use/transportation models embed a transportation demand module or sub model for translating magnitude of activities and interconnections into travel demand such as VMT, ridership, congestion, toll usage, etc. Four-step models can be categorized as gravity-based, equilibrium-based models from the traditional approaches. To improve these models, several new extensions has been developed such as simultaneous mode and destination choice, multimodality (more options for mode choice with utility), or microsimulation models that improve granularity of models by representing individuals or agents rather than zones or neighborhoods.

Travel demand modeling are models that predicts the flow of traffic or travel demand between zones in a city using a sequence of steps.

Intermodality refers to the concept of utilizing two or more travel modes for a trip such as biking to a transit station and riding the light rail.
Multimodality is a type of transportation network in which a variety of modes such as public transit, rail, biking networks, etc. are offered.

Zoning ordinances is legal categorization of land use policies that permits or prohibits certain built environment factors such as density.

Volume capacity ratio is ratio that divides the demand on a link by the capacity to determine the level of service.

Zone centroid is usually the geometric center of a zone in modeling process where all trips originate and end.

Home-based work trips (HBW) are the trips that originates from home location to work location usually in the AM peak.

Home-based other (or non-work) trips (HBO) are the trips that originates from home to destinations other than work like shopping or leisure.

Non-home-based trips (NHB) are the trips that neither origin nor the destination are home or they are part of a linked trip.

Cross-classification is a method for trip production estimation that disaggregates trip rates in an extended format for different categories of trips like home-based trips or non-home-based trips and different attributes of households such as car ownership or income.

Generalized travel costs is a function of time divided into sections such as in vehicle time vs. waiting time or transfer time in a transit trip.

Binary logit models is a type of logit model where the dependent variable can take only a value of 0 or 1.

Wardrop equilibrium is a state in traffic assignment model where are drivers are reluctant to change their path because the average travel time is at a minimum.

All-or-nothing (AON) assignment model is a model that assumes all trips between two zones uses the shortest path regardless of volume.

Speed flow relationship is a function that determines the speed based on the volume (flow)

skim tree is structure of travel time by defining minimum cost path for each section of a trip.

Key Takeaways

In this chapter, we covered:

What travel demand modeling is for and what the common methods are to do that.
How FSM is structured sequentially, what the relationships between different steps are, and what the outputs are.
What the advantages and disadvantages of different methods and assumptions in each step are.
What certain data collection and preparation for trip generation and distribution are needed through a hypothetical example.

Prep/quiz/assessments

What is the need for regular travel demand forecasting, and what are its two major components?
Describe what data we require for each of the four steps.
What are the advantages and disadvantages of regression and cross-classification methods for a trip generation?
What is the most common modeling framework for mode choice, and what result will it provide us?
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Home-based other (or non-work) trips (HBO) are the trips that originates from home to destinations other than work like shopping or leisure.

gravity model is a type of accessibility measurement in which the employment in destination and population in the origin defines thee degree of accessibility between the two zones.

Impedance function is a function that convert travel costs (usually time or distance) to the level of difficulty of getting from one location to the other.

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Travel Demand Forecasting: Parameters and Techniques (2012)

Chapter: chapter 1 - introduction.

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1 1.1 Background In 1978, the Transportation Research Board (TRB) published NCHRP Report 187: Quick-Response Urban Travel Estimation Techniques and Transferable Parameters (Sosslau et al., 1978). This report described default parameters, factors, and manual techniques for doing planning analysis. The report and its default data were used widely by the transportation planning profession for almost 20 years. In 1998, drawing on several newer data sources, including the 1990 Census and Nation- wide Personal Transportation Survey, an update to NCHRP Report 187 was published in the form of NCHRP Report 365: Travel Estimation Techniques for Urban Planning (Martin and McGuckin, 1998). Since NCHRP Report 365 was published, significant changes have occurred affecting the complexity, scope, and context of transportation planning. Transportation planning tools have evolved and proliferated, enabling improved and more flexible analyses to support decisions. The demands on trans- portation planning have expanded into special populations and broader issues (e.g., safety, congestion, pricing, air quality, environment, climate change, and freight). In addition, the default data and parameters in NCHRP Report 365 need to be updated to reflect the planning requirements of today and the next 10 years. The objective of this report is to revise and update NCHRP Report 365 to reflect current travel characteristics and to pro- vide guidance on travel demand forecasting procedures and their application for solving common transportation problems. It is written for âmodeling practitioners,â who are the public agency and private-sector planners with responsibility for devel- oping, overseeing the development of, evaluating, validating, and implementing travel demand models. This updated report includes the optional use of default parameters and appropriate references to other more sophisticated techniques. The report is intended to allow practitioners to use travel demand fore- casting methods to address the full range of transportation planning issues (e.g., environmental, air quality, freight, multimodal, and other critical concerns). One of the features of this report is the provision of trans- ferable parameters for use when locally specific data are not available for use in model estimation. The parameters pre- sented in this report are also useful to practitioners who are modeling urban areas that have local data but wish to check the reasonableness of model parameters estimated from such data. Additionally, key travel measures, such as average travel times by trip purpose, are provided for use in checking model results. Both the transferable parameters and the travel measures come from two main sources: the 2009 National Household Travel Survey (NHTS) and a database of model documentation for 69 metropolitan planning organizations (MPOs) assembled for the development of this report. There are two primary ways in which planners can make use of this information: 1. Using transferable parameters in the development of travel model components when local data suitable for model development are insufficient or unavailable; and 2. Checking the reasonableness of model outputs. This report is written at a time of exciting change in the field of travel demand forecasting. The four-step modeling process that has been the paradigm for decades is no longer the only approach used in urban area modeling. Tour- and activity-based models have been and are being developed in several urban areas, including a sizable percentage of the largest areas in the United States. This change has the potential to significantly improve the accuracy and analytical capability of travel demand models. At the same time, the four-step process will continue to be used for many years, especially in the smaller- and medium- sized urban areas for which this report will remain a valuable resource. With that in mind, this report provides information on parameters and modeling techniques consistent with the C h a p t e r 1 Introduction

2four-step process and Chapter 4, which contains the key information on parameters and techniques, is organized con- sistent with the four-step approach. Chapter 6 of this report presents information relevant to advanced modeling practices, including activity-based models and traffic simulation. This report is organized as follows: â¢ Chapter 1âIntroduction; â¢ Chapter 2âPlanning Applications Context; â¢ Chapter 3âData Needed for Modeling; â¢ Chapter 4âModel Components: â Vehicle Availability, â Trip Generation, â Trip Distribution, â External Travel, â Mode Choice, â Automobile Occupancy, â Time-of-Day, â Freight/Truck Modeling, â Highway Assignment, and â Transit Assignment; â¢ Chapter 5âModel Validation and Reasonableness Checking; â¢ Chapter 6âEmerging Modeling Practices; and â¢ Chapter 7âCase Studies. This report is not intended to be a comprehensive primer for persons developing a travel model. For more complete information on model development, readers may wish to consult the following sources: â¢ âIntroduction to Urban Travel Demand Forecastingâ (Federal Highway Administration, 2008); â¢ âIntroduction to Travel Demand Forecasting Self- Instructional CD-ROMâ (Federal Highway Administra- tion, 2002); â¢ NCHRP Report 365: Travel Estimation Techniques for Urban Planning (Martin and McGuckin, 1998); â¢ An Introduction to Urban Travel Demand Forecastingâ A Self-Instructional Text (Federal Highway Administration and Urban Mass Transit Administration, 1977); â¢ FSUTMS Comprehensive Modeling Online Training Workshop (http://www.fsutmsonline.net/online_training/ index.html#w1l3e3); and â¢ Modeling Transport (Ortuzar and Willumsen, 2001). 1.2 Travel Demand Forecasting: Trends and Issues While there are other methods used to estimate travel demand in urban areas, travel demand forecasting and mod- eling remain important tools in the analysis of transportation plans, projects, and policies. Modeling results are useful to those making transportation decisions (and analysts assisting in the decision-making process) in system and facility design and operations and to those developing transportation policy. NCHRP Report 365 (Martin and McGuckin, 1998) pro- vides a brief history of travel demand forecasting through its publication year of 1998; notably, the evolution of the use of models from the evaluation of long-range plans and major transportation investments to a variety of ongoing, every- day transportation planning analyses. Since the publication of NCHRP Report 365, several areas have experienced rapid advances in travel modeling: â¢ The four-step modeling process has seen a number of enhancements. These include the more widespread incor- poration of time-of-day modeling into what had been a process for modeling entire average weekdays; common use of supplementary model steps, such as vehicle availability models; the inclusion of nonmotorized travel in models; and enhancements to procedures for the four main model components (e.g., the use of logit destination choice models for trip distribution). â¢ Data collection techniques have advanced, particularly in the use of new technology such as global positioning systems (GPS) as well as improvements to procedures for performing household travel and transit rider surveys and traffic counts. â¢ A new generation of travel demand modeling software has been developed, which not only takes advantage of modern computing environments but also includes, to various degrees, integration with geographic information systems (GIS). â¢ There has been an increased use of integrated land use- transportation models, in contrast to the use of static land use allocation models. â¢ Tour- and activity-based modeling has been introduced and implemented. â¢ Increasingly, travel demand models have been more directly integrated with traffic simulation models. Most travel demand modeling software vendors have developed traffic simulation packages. At the same time, new transportation planning require- ments have contributed to a number of new uses for models, including: â¢ The analysis of a variety of road pricing options, including toll roads, high-occupancy toll (HOT) lanes, cordon pricing, and congestion pricing that varies by time of day; â¢ The Federal Transit Administrationâs (FTAâs) user benefits measure for the Section 5309 New Starts program of transit projects, which has led to an increased awareness of model properties that can inadvertently affect ridership forecasts;

3 â¢ The evaluation of alternative land use patterns and their effects on travel demand; and â¢ The need to evaluate (1) the impacts of climate change on transportation supply and demand, (2) the effects of travel on climate and the environment, and (3) energy and air quality impacts. These types of analyses are in addition to several traditional types of analyses for which travel models are still regularly used: â¢ Development of long-range transportation plans; â¢ Highway and transit project evaluation; â¢ Air quality conformity (recently including greenhouse gas emissions analysis); and â¢ Site impact studies for developments. 1.3 Overview of the Four-Step Travel Modeling Process The methods presented in this report follow the conven- tional sequential process for estimating transportation demand that is often called the âfour-stepâ process: â¢ Step 1âTrip Generation (discussed in Section 4.4), â¢ Step 2âTrip Distribution (discussed in Section 4.5), â¢ Step 3âMode Choice (discussed in Section 4.7), and â¢ Step 4âAssignment (discussed in Sections 4.11 and 4.12). There are other components commonly included in the four-step process, as shown in Figure 1.1 and described in the following paragraphs. The serial nature of the process is not meant to imply that the decisions made by travelers are actually made sequentially rather than simultaneously, nor that the decisions are made in exactly the order implied by the four-step process. For example, the decision of the destination for the trip may follow or be made simultaneously with the choice of mode. Nor is the four-step process meant to imply that the decisions for each trip are made independently of the decisions for other trips. For example, the choice of a mode for a given trip may depend on the choice of mode in the preceding trip. In four-step travel models, the unit of travel is the âtrip,â defined as a person or vehicle traveling from an origin to a destination with no intermediate stops. Since people traveling for different reasons behave differently, four-step models segment trips by trip purpose. The number and definition of trip purposes in a model depend on the types of information the model needs to provide for planning analyses, the char- acteristics of the region being modeled, and the availability of data with which to obtain model parameters and the inputs to the model. The minimum number of trip purposes in most models is three: home-based work, home-based nonwork, and nonhome based. In this report, these three trip purposes are referred to as the âclassic threeâ purposes. The purpose of trip generation is to estimate the num- ber of trips of each type that begin or end in each location, based on the amount of activity in an analysis area. In most models, trips are aggregated to a specific unit of geography (e.g., a traffic analysis zone). The estimated number of daily trips will be in the flow unit that is used by the model, which is usually one of the following: vehicle trips; person trips in motorized modes (auto and transit); or person trips by all modes, including both motorized and nonmotorized (walking, bicycling) modes. Trip generation models require some explanatory variables that are related to trip-making behavior and some functions that estimate the number of trips based on these explanatory variables. Typical variables include the number of households classified by characteristics such as number of persons, number of workers, vehicle availability, income level, and employment by type. The output of trip generation is trip productions and attractions by traffic analysis zone and by purpose. Trip distribution addresses the question of how many trips travel between units of geography (e.g., traffic analysis zones). In effect, it links the trip productions and attractions from the trip generation step. Trip distribution requires explanatory variables that are related to the cost (including time) of travel between zones, as well as the amount of trip-making activity in both the origin zone and the destination zone. The outputs of trip distribution are production-attraction zonal trip tables by purpose. Models of external travel estimate the trips that originate or are destined outside the modelâs geographic region (the model area). These models include elements of trip generation and distribution, and so the outputs are trip tables represent- ing external travel. Mode choice is the third step in the four-step process. In this step, the trips in the tables output by the trip distri- bution step are split into trips by travel mode. The mode definitions vary depending on the types of transportation options offered in the modelâs geographic region and the types of planning analyses required, but they can be generally grouped into auto mobile, transit, and nonmotorized modes. Transit modes may be defined by access mode (walk, auto) and/or by service type (local bus, express bus, heavy rail, light rail, commuter rail, etc.). Nonmotorized modes, which are not yet included in some models, especially in smaller urban areas, include walking and bicycling. Auto modes are often defined by occupancy levels (drive alone, shared ride with two occupants, etc.). When auto modes are not modeled separately, automobile occupancy factors are used to convert the auto person trips to vehicle trips prior to assignment. The outputs of the mode choice process include person trip tables by mode and purpose and auto vehicle trip tables.

4Time-of-day modeling is used to divide the daily trips into trips for various time periods, such as morning and afternoon peak periods, mid-day, and evening. This division may occur at any point between trip generation and trip assignment. Most four-step models that include the time-of-day step use fixed factors applied to daily trips by purpose, although more sophisticated time-of-day choice models are sometimes used. While the four-step process focuses on personal travel, commercial vehicle/freight travel is a significant component of travel in most urban areas and must also be considered in the model. While simple factoring methods applied to per- sonal travel trip tables are sometimes used, a better approach is to model such travel separately, creating truck/commercial vehicle trip tables. The final step in the four-step process is trip assignment. This step consists of separate highway and transit assignment processes. The highway assignment process routes vehicle trips from the origin-destination trip tables onto paths along Forecast Year Highway Network Forecast Year Transit Network Forecast Year Socioeconomic DataTrip Generation Model Internal Productions and Attractions by Purpose Trip Distribution Model Mode Choice Model Person and Vehicle Trip Tables by Purpose/Time Period Time of Day Model Person and Vehicle Trip Tables by Mode/Purpose/Time Period Highway Assignment CHECK: Input and output times consistent? Transit Assignment Highway Volumes/ Times by Time Period Transit Volumes/ Times by Time Period Input Data Model Output Model Component Decision Feedback Loop Yes No Truck Trip Generation and Distribution Models Production/Attraction Person Trip Tables by Purpose Truck Vehicle Trip Tables by Purpose Truck Time of Day Model Truck Vehicle Trip Tables by Time Period External Trip Generation and Distribution Models External Vehicle Trip Tables by Time Period Figure 1.1. Four-step modeling process.

5 the highway network, resulting in traffic volumes on network links by time of day and, perhaps, vehicle type. Speed and travel time estimates, which reflect the levels of congestion indicated by link volumes, are also output. The transit assignment process routes trips from the transit trip tables onto individual transit routes and links, resulting in transit line volumes and station/ stop boardings and alightings. Because of the simplification associated with and the resul- tant error introduced by the sequential process, there is some- times âfeedbackâ introduced into the process, as indicated by the upward arrows in Figure 1.1 (Travel Model Improvement Program, 2009). Feedback of travel times is often required, particularly in congested areas (usually these are larger urban areas), where the levels of congestion, especially for forecast scenarios, may be unknown at the beginning of the process. An iterative process using output travel times is used to rerun the input steps until a convergence is reached between input and output times. Because simple iteration (using travel time outputs from one iteration directly as inputs into the next iteration) may not converge quickly (or at all), averaging of results among iterations is often employed. Alternative approaches include the method of successive averages, constant weights applied to each iteration, and the Evans algorithm (Evans, 1976). Although there are a few different methods for implement- ing the iterative feedback process, they do not employ param- eters that are transferable, and so feedback methods are not discussed in this report. However, analysts should be aware that many of the analysis procedures discussed in the report that use travel times as inputs (for example, trip distribution and mode choice) are affected by changes in travel times that may result from the use of feedback methods. 1.4 Summary of Techniques and Parameters Chapter 4 presents information on (1) the analytical tech- niques used in the various components of conventional travel demand models and (2) parameters for these mod- els obtained from typical models around the United States and from the 2009 NHTS. These parameters can be used by analysts for urban areas without sufficient local data to use in estimating model parameters and for areas that have already developed model parameters for reasonableness checking. While it is preferable to use model parameters that are based on local data, this may be impossible due to data or other resource limitations. In such cases, it is common practice to transfer parameters from other applicable models or data sets. Chapter 4 presents parameters that may be used in these cases, along with information about how these parameters can be used, and their limitations. 1.5 Model Validation and Reasonableness Checking Another important use of the information in this report will be for model validation and reasonableness checking. There are other recent sources for information on how the general process of model validation can be done. Chapter 5 provides basic guidance on model validation and reasonable- ness checking, with a specific focus on how to use the informa- tion in the report, particularly the information in Chapter 4. It is not intended to duplicate other reference material on validation but, rather, provide an overview on validation consistent with the other sources. 1.6 Advanced Travel Analysis Procedures The techniques and parameters discussed in this report focus on conventional modeling procedures (the four-step process). However, there have been many recent advances in travel modeling methods, and some urban areas, especially larger areas, have started to use more advanced approaches to modeling. Chapter 6 introduces concepts of advanced model- ing procedures, such as activity-based models, dynamic traffic assignment models, and traffic simulation models. It is not intended to provide comprehensive documentation of these advanced models but rather to describe how they work and how they differ from the conventional models discussed in the rest of the report. 1.7 Case Study Applications One of the valuable features in NCHRP Report 365 was the inclusion of a case study to illustrate the application of the parameters and techniques contained in it. In this report, two case studies are presented to illustrate the use of the information in two contexts: one for a smaller urban area and one for a larger urban area with a multimodal travel model. These case studies are presented in Chapter 7. 1.8 Glossary of Terms Used in This Report MPOâMetropolitan Planning Organization, the federally designated entity for transportation planning in an urban area. In most areas, the MPO is responsible for maintaining and running the travel model, although in some places, other agencies, such as the state department of transportation, may have that responsibility. In this report, the term âMPOâ is sometimes used to refer to the agency responsible for the model, although it is recognized that, in some areas, this agency is not officially the MPO.

6Model areaâThe area covered by the travel demand model being referred to. Often, but not always, this is the area under the jurisdiction of the MPO. The boundary of the model area is referred to as the cordon. Trips that cross the cordon are called external trips; modeling of external trips is discussed in Section 4.6. Person tripâA one-way trip made by a person by any mode from an origin to a destination, usually assumed to be without stops. In many models, person trips are the units used in all model steps through mode choice. Person trips are the usual units in transit assignment, but person trips are converted to vehicle trips for highway assignment. Trip attractionâIn four-step models, the trip end of a home-based trip that occurs at the nonhome location, or the destination end of a nonhome-based trip. Trip productionâIn four-step models, the trip end of a home-based trip that occurs at the home, or the origin end of a nonhome-based trip. Vehicle tripâA trip made by a motorized vehicle from an origin to a destination, usually assumed to be without stops. It may be associated with a more-than-one-person trip (for example, in a carpool). Vehicle trips are the usual units in highway assignment, sometimes categorized by the number of passengers per vehicle. In some models, vehicle trips are used as the units of travel throughout the modeling process. Motorized and nonmotorized tripsâMotorized trips are the subset of person trips that are made by auto or transit, as opposed to walking or bicycling trips, which are referred to as nonmotorized trips. In-vehicle timeâThe total time on a person trip that is spent in a vehicle. For auto trips, this is the time spent in the auto and does not include walk access/egress time. For transit trips, this is the time spent in the transit vehicle and does not include walk access/egress time, wait time, or time spent transferring between vehicles. Usually, transit auto access/ egress time is considered in-vehicle time. Out-of-vehicle timeâThe total time on a person trip that is not spent in a vehicle. For auto trips, this is usually the walk access/egress time. For transit trips, this is the walk access/ egress time, wait time, and time spent transferring between vehicles. In some models, components of out-of-vehicle time are considered separately, while in others, a single out-of- vehicle time variable is used.

TRB’s National Cooperative Highway Research Program (NCHRP) Report 716: Travel Demand Forecasting: Parameters and Techniques provides guidelines on travel demand forecasting procedures and their application for helping to solve common transportation problems.

The report presents a range of approaches that are designed to allow users to determine the level of detail and sophistication in selecting modeling and analysis techniques based on their situations. The report addresses techniques, optional use of default parameters, and includes references to other more sophisticated techniques.

Errata: Table C.4, Coefficients for Four U.S. Logit Vehicle Availability Models in the print and electronic versions of the publications of NCHRP Report 716 should be replaced with the revised Table C.4 .

NCHRP Report 716 is an update to NCHRP Report 365 : Travel Estimation Techniques for Urban Planning .

In January 2014 TRB released NCHRP Report 735 : Long-Distance and Rural Travel Transferable Parameters for Statewide Travel Forecasting Models , which supplements NCHRP Report 716.

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What is TRICS and how does it work?

TRICS® is the system that challenges and validates assumptions about the transport impacts of new developments

What is TRICS®?

TRICS® is the system of trip generation analysis for the UK and Ireland. First launched in 1989, it is an integral and essential part of the Transport Assessment process, and through continuous investment and development it has expanded into a comprehensive database of traffic and multi-modal transport surveys, covering a wide range of development types.

The system allows its users to establish potential levels of trip generation for their development scenarios using a series of database filtering processes, and it is widely used by both transport planning consultants and local authorities (the latter of which use TRICS® to audit Transport Assessments).

TRICS® contains...

The TRICS® database includes over 8,000 transport surveys. In addition to inbound and outbound traffic and multi-modal counts (covering a wide range of separate count and mode types), the TRICS® site records include comprehensive descriptive detail on a site's local environment and surroundings, information on the size, composition and functions of a site, and details of on-site and off-site parking facilities. Large annual regional data collection programmes across all parts of the UK and Ireland ensure that new transport surveys are continuously added to the database.

As well as being a database of transport surveys, TRICS® is also a system that allows its users to apply inclusion criteria to calculate trip rates for their various development planning scenarios, and they can do so through a number of progressive and user-friendly filtering stages. TRICS® is constantly reviewed, and through a successful interactive and inclusive approach, feedback from member organisations assists in the constant development of new system features and enhancements. The system also includes an easily accessible directory of help files to assist users in their understanding of the database and its operations.

How is it managed?

TRICS® Consortium Limited is a company wholly owned by Dorset Council, Hampshire County Council, East Sussex County Council, Kent County Council, Surrey County Council, and West Sussex County Council. These local authorities constitute the Board of the company, and TRICS is project managed and delivered by a separate team of management and technical staff based in London.

What is the best way to use TRICS®

The TRICS® Good Practice Guide provides detailed guidance on all elements of the TRICS® system procedures and its survey data, and is considered essential reading by all user organisations. It has been written to encourage users to operate the system and present its data in the most correct way, and it is also a very handy reference for those tasked with auditing TRICS® results that are presented in Transport Assessments and other documents.

View the current version of the TRICS® Good Practice Guide here

Using the System

System requirements, trics® supported browsers.

macOS 11/12/13/14: Safari/Chrome
Windows 10/11: Microsoft Edge/Chrome
This can be installed as a service onto a Windows PC running Windows 10 or 11.
It requires 700mb of disk space and 200mb of RAM.
The Offline version cannot be installed on a Mac.

Supply and Commission of TRICS® Data

TRICS® directly commissions a large annual data collection programme of traffic and multi-modal surveys across all regions of the UK and Ireland, covering a wide variety of development types. This ongoing commitment to comprehensive continuous data collection programmes is what makes TRICS® the standard system of trip generation analysis.

We can also manage and deliver individual TRICS® surveys upon request, as a direct commission from any organisation. Working with our TRICS®-approved data collection contractors, this process involves undertaking site visits, producing detailed survey specifications, undertaking the multi-modal survey counts, and the subsequent delivery of a set of survey results to a fully TRICS®-certified standard (following a comprehensive process of validation testing). Numerous organisations commission TRICS® to manage and deliver surveys at a wide range of development types for the purposes of travel plan monitoring. This process is known as SAM (Standardised Assessment Methodology).

Organisations can also supply their own surveys should they wish them to be certified as TRICS®-compliant. Our multi-modal data collection methodology and data collection standard can be adopted should an organisation wish to manage and undertake a survey themselves, and such surveys would be considered TRICS®-compliant following the subsequent data input and validation process undertaken by the TRICS® team. This data would then be added to the TRICS® database, and the input and validation fees for these surveys are £160 + VAT for a traffic count, £207 + VAT for a multi-modal count, and £266 + VAT for a multi-modal count with travel plan information included. Your survey data being certified as TRICS®-compliant would be of great benefit to your organisation as it would receive recognition of compliance from the standard system of trip generation analysis that is widely recognised around the UK and Ireland (subject to your data being successfully validated of course). You would also receive a certificate of TRICS®-compliance upon the process being completed. The TRICS® multi-modal methodology document can be downloaded here

How to use TRICS®

If an organisation is tasked with calculating potential traffic and transport generation for a future proposed development, then they can use TRICS® to undertake this exercise. The system has a lot of data contained within it as surveys have been continuously undertaken over a long period of time, and as a result it is very flexible, allowing users to operate a wide range of database filtering processes. The system is designed to take users through progressive stages of this filtering, starting off with the full database for a selected development type until all of the compatibility criteria (decided upon by the user) have been met, with users ending up with a smaller, compatible set of surveys ready to undergo a trip rate calculation. The result is a range of trip generation rates for a development scenario, which can then be used in a Transport Assessment.

Individual trip rates for a survey period for a set of surveys can also be calculated and ranked in order of relative trip rate intensity, in what we call a rank order list. Accompanying trip rate graphs and rank order scatterplots can provide users with visual representations of peak trip activity and visual relationships between trip rate calculation parameters (for example Gross Floor Area, Number of Dwellings, etc) and trips.

System Navigation

As the TRICS® database is large (currently over 8,000 survey days), it is important that its users are provided with an easy method of system navigation. When using TRICS®, users can select to view database site lists by choosing their required development type. TRICS® provides user-friendly drop-down menus for this purpose.

When viewing individual site records from site lists, a system of icons and tabs allows users to move freely through the comprehensive set of descriptive information that accompanies the survey counts. Separate screens within a site record display site location information, details on local public transport accessibility, design features encouraging non-car modes of transport, development details such as the size and functions of a site, and information on on-site parking and local off-site parking availability. All of this is supplemented by further descriptive detail in dedicated comment areas within individual sites, so users can get a thorough picture of where a site is, its local environment, and what it consists of.

The trip rate calculation process also follows a logical process of filtering, broken down into easy-to-follow stages leading up to the actual calculation once users are satisfied with their selection criteria. It is important for us that movement through the system at all its stages is fluid and user-friendly, and we are constantly developing and refining the system to ensure that this remains the case.

Trip Rate Calculations

TRICS® users undertake trip rate calculations using a number of calculation parameter options (for example Gross Floor Area, Dwellings, Employees, etc), to ascertain potential levels of trip generation for a user-defined development scenario.

What are trip rates?

Trip rates show the number of traffic/people movements to and from a development (or an average for selected developments within the same land use sub-category), for a selected trip rate parameter factor. For example, when trip rates are calculated by Gross Floor Area (GFA), they are presented per 100m2 of GFA. Using this factor, users can apply trip rates to their development scenarios, and are encouraged to achieve a balance between their selection criteria and the size of their selected data sample to achieve this.

How are they calculated?

Average (mean) trip rates are calculated when there are at least 2 surveys included in a selected set (trip rates for an individual site can also be calculated). The calculation process consists of 3 parts, and these apply to every period of the survey duration, split by inbound, outbound, and total (two-way) trips.

Stage 1: Obtaining average (mean) trip rate parameter figures

Stage 2: obtaining average (mean) traffic/people count figures, stage 3: calculating trip rates from the result of stages 1 and 2.

( m ARR / m TRP) X 100

This calculates the average (mean) trip rate per 100m2 of GFA, and this figure is then shown within the trip rate calculation results table. The calculations for DEPARTURES and TOTALS are similar, as shown below:

DEPARTURES = ( m DEP / m TRP) X 100

TOTALS = ( m TOT / m TRP) X 100

This method calculates the trip rate per 100m2 of GFA for any survey period. Note that the calculation factor (100m2 in the case of GFA) varies, depending on the trip rate parameter used (e.g. per dwelling, per employee, per hectare, etc).

Trip rate graphs that provide a visual representation of the trip rate results table can also be produced at a single click, and can be used to identify percentages of trips per period as a proportion of the whole survey duration, as well as indicate periods of peak trip activity.

Trip rate rank order lists display the trip rate value (either arrivals, departures or totals) for each individual survey included in the original trip rate calculation, with the survey with the highest relative trip rate (per calculation factor) shown at the top of the list. Rank order list trip rates are calculated for a selected period, including each individual survey in a selected set, so are a good way to view trip rates for on a like-for-like basis without any averaging taking place, also providing a good example of the range of trip rates through a selected set of surveys.

Note that trip rates can be calculated for the whole range of different TRICS® count types, for example Total Vehicles, Pedestrians, Public Transport Users, Total People.

System Updates

The TRICS® system is updated on a quarterly basis and automatically updates on-line. Typically, updates of the system are made live within the months of March, July, September, and December.

Ongoing development of the system takes place through an inclusive and interactive approach that encourages the input of our member organisations, collectively known as the TRICS® Community. Through our popular annual User Meetings and Training & Development Forums, responses to our annual User Surveys, and through direct correspondence with our users, the system is continuously developed and improved. When new versions of the system are ready to go live, users are notified of all system changes and new surveys that have been added to the database.

An annual, comprehensive programme of data collection, covering all regions of the UK and Ireland, also means that new surveys are usually added to the database at the time of each update. All data supplied to TRICS® by our approved data collection contractors is subject to thorough, stringent validation procedures, with no new data being accepted into the database until all our validation testing criteria has been met. This ensures that TRICS® data remains of the highest quality.

User training & demonstrations

Good practice guide, intellectual property rights.

The survey data, graphs and all associated supporting information, contained within the TRICS Database are created and published by TRICS Consortium Limited ("the Company") and the Company claims copyright and database rights in this published work. Use of this data is restricted to current TRICS licence holders and those using the TRICS Bureau Service. Licence holders may publish data from the TRICS Database in accordance with the TRICS licence. TRICS data, or extracts of TRICS data, should only be provided to third parties as part of a complete planning application document or in accordance with the Company's Bureau Services or the members' terms of licence. Data should not be attained by copying extracts from previously published reports or other documents.

Any use, publication or distribution of data owned by the Company, whether obtained from the TRICS Database or from third party operated planning portals, without the Company's authorisation or a valid TRICS licence (other than in accordance with the Company's Bureau Service), will infringe the Company's intellectual property rights in the data. Please be aware that any unauthorised use of data contained in online planning portals may constitute a breach of the third party's terms of use and/or an infringement of its intellectual property rights.

TRICS is marketed and managed by TRICS Consortium Limited, Suite 10, Ashdon House, Moon Lane, Barnet, London EN5 5YL

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Trip Generation

Trip Generation is the first step in the Sequential Demand Modelling arrangement which is also called as the Four Step Transportation Planning Process(FSTP) as mentioned earlier. In order to carry out modelling, the variable consists of total number of person-trips generated by a zone as a dependent variable and the independent variable consists of household and socio- economic factors which influence the trip making behaviour of the person. The data for the independent variable should be attained from an analyst. The output thus obtained consists of trip making or trip ends for each zone within a region.

In contemporary transportation planning language, A Trip is defined as a one way person movement by a mechanized mode of transport, having two trip ends. The start of the trip is called as origin and the end of trip is called as destination. Trip is classified as Production or Origin and Attraction or Destination. It should be note that the terminologies used are not identical. To understand with an example consider a single worker on a typical working day making a trip from his house which is in zone P to his office in Zone Q . Thus his trip origin will be zone P and trip destination will be zone Q . For the return trip from office to house his trip origin will be zone Q and trip Destination will be Zone P . Thus from the above Example it can be understood that the term Origin and Destination are defined in terms of direction of the trip while Production and Attraction in terms of land use associated with each trip end. Trip Production is the home end of home based trip and is the origin of trip of non home based trip. Trip Attraction is the non home end of home based trip and is the destination of a non home based trip.

It has been found that better trip generation models can be obtained if the trips by different purpose are identified and modelled separately.The trips can be classified as given below:

1. Home Based Trip: One of the trip end is home.

Example: A trip from home to office.

Following are the list of home based trips that is trip purpose which are classified into five categories:

a. Work Trips

b. School Trips

c. Shopping Trips

d. Social- recreational Trips

e. Other Trips

The first two trips are mandatory trips while other trips are discretional trips. The other trip class encompasses all the trips made for less routine purpose such as health bureaucracy etc.

2. Non Home based trips: None of the trip end is home.

Example: A trip from office to Shopping Mall.

3. Time based trips

The proportion of journey is different by different purposes usually varies with time of the day. Thus the classification is often given as Peak and Off Peak Period Trip.

4. Person-type based trips

The travel behaviour of an individual is mainly dependent on its Socio-Economic attributes. Following are the categories which are usually employed.

a. Income Level- Poor, Middle Class, Rich

b. Car Ownership- 0,1,2,3

c. Household Size- 1,2,3,4... etc

a. No. of workers in a household.

b. No. of Students.

c. Household size and composition.

d. The household income.

e. Some proxy of income such as number of cars etc.

a. Floor area and number of employment opportunities in retail trade, service, offices manufacturing and wholesale areas.

b. School and college enrolment

c. Other activity centres like transport terminals, sports stadium, major recreational/ cultural/religious places

Table below represents base year data of Trip Production for exact zone.

Similarly Trip Attraction Table is obtained with respect to its influencing variables.

Trip generation study typically involves the application of residential trip production which contains variable that defines the demographic makeup of zonal population and trip attraction that captures the activity of non residential activities within the zone.

In the example given below the zones are connected by a two way link. Each zone will have its own demographic and non residential characteristics depending on which the Trip Generation table is represented below.

Modelling basically relates the dependent variable ie trips produced by a zone for aggregated model or household trip production rate for household based models to the corresponding Independent variables characterised by the whole zone or household characteristic respectively. Calibration is done based on the set of observations obtained corresponding to the zones for aggregate model and for disaggregate model employs a number of base year observations corresponding to an individual household in a sample of household drawn randomly from the region. Thus we first need to identify what are the relevant variables: a. Home end b. Work End c. Shop End

Analytical tools used for Trip Generation Modelling are given below: 1. Regression Model (Regression Analysis) 2. Cross Classification Model (Category Analysis)

The purpose of trip generation is to estimate the number of trip ends for each zones for the targeted year. The trip end is calculated for different travel purpose within the zone. These trips are represented as residential trip production obtained from household based cross classification tables or non residential trip attractions which is obtained by projection of land use. Trip generation Models that are often used are Multiple Linear Regression Model or Cross Classification Model or involves combination of both.

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Making the Most of Skip-Generational Trips

For grandparents traveling with grandchildren, it’s a chance to bond and create shared memories.

grandparents with their grandchildren on a Road Scholar boat tour

Cheryl Maguire,

One of the travel trends to look for in 2024 is skip-generation trips . This is when grandparents travel with their grandchild sans children, thereby skipping a generation.

According to the 2023 U.S. Family Travel Survey, grandparents choose to take a skip-generational trip to be able to bond with their grandchild. In the survey from the Family Travel Association, grandparents said that after taking this type of trip, they think their grandchild is more adventurous and that the trip helped their grandchild be more flexible and adaptable.

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We spoke to travel experts to find the best tips and types of travel for your skip-generation trip.

1. Talking with your child about traveling without them

“The initial conversations have to be between the grandparent and the parents, without the grandchild involved, and just make sure that the parents are comfortable with it,” says Kenneth Shapiro, the board president of the Family Travel Association. He explains that typically parents are excited that their child and parent are going to take a trip together. In most cases the grandparent pays for the vacation, so their child doesn’t need to worry about expenses. Also, if the grandparents take their grandchild on the trip during their school vacation or the summer, the parent doesn’t need to take time off from their job. “It’s a win-win for everybody,” says Shapiro.

Grandparents can explain that this trip is an opportunity to spend time with their grandchild. “I think it’s a chance to just say that [the grandparents] want to bond in a special way,” says Darley Newman, the host and executive producer of Travels with Darley .

2. Explaining expectations during the trip

If a grandparent doesn’t live with their grandchild, they will need to set expectations with them before they travel. “There are certain situations that an adult needs to be in charge. You can’t just let the kids run the show all the time,” says Shapiro.

He explains that the grandparents should ask for their grandchild’s input during the planning stages. “I think that’s an opportunity to give the grandchild some responsibility and also a chance to have fun,” he says. If you allow the grandchild to collaborate during the planning process, then they will be able to select activities or destinations based on their interests. “The more that the grandchild’s involved [in the planning], the more special I think the trip is going to be,” says Jessica Griscavage, a senior travel adviser and the founder of Runway Travel.

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3. Consider activity levels, mobility needs and downtime

Shelby Dziwulski, founder and CEO of the Denver-based travel company Authenteco explains that it is important to understand the limitations at your destination related to physical activity, safety and mobility. “It really does change where you’re sending people,” she says. For example, if you have mobility needs she might suggest a location that has accessibility features such as elevators or wheelchair ramps instead of a place with a lot of stairs or cobblestone roads. Another key factor is incorporating breaks or downtime into your trip. No matter your age, if you don’t take a break you’ll be too tired to enjoy the trip. “For any family travel, you have to add some downtime,” Griscavage says.

4. Consider these trips for skip-generation travel

You can take a skip-generational trip anywhere, but experts suggest these types of trips are typically set up to accommodate you.

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Exploring ancestry and heritage

Heritage tourism , a type of travel that traces your roots , has become popular in recent years due to DNA services that track your lineage. “I think it’s a great opportunity to learn more about yourself and the world that you’re in at the same time,” Shapiro says.

Before your heritage trip, you can research your genealogy and then meet relatives or travel to places based on your ancestry. “It’s not until you’re actually there tasting the local food and seeing the sights that you understand that location in that intrinsic way. It can change the way that you think about yourself,” Newman says.

Ocean or river cruises

Cruises are an all-inclusive accommodation and meal option that easily allow you to see multiple destinations. “You unpack once and you get to visit multiple countries,” says Griscavage. Cruise lines such as Royal Caribbean Cruises , Norwegian Cruise Line and Disney Cruise also offer group activities, entertainment and excursions. Kids or teen clubs offered on some cruises could provide a chance for the grandparent to take a break if needed. Another bonus is that grandparents can easily keep track of their grandchild on river cruises such as A-ROSA Cruises , Uniworld or Viking River Cruises . “It’s a contained and safe environment,” says Newman.

All-inclusive resorts

Similar to a cruise vacation, an all-inclusive resort , such as Beaches , Club Med or Nickelodeon Resorts , offers activities and entertainment in a hotel instead of on a cruise ship. Many of these resorts also offer a kid or teen club. If you get seasick or prefer to stay in one location, this may be a better option than a cruise.

A ranch trip offers a Western lifestyle experience including fishing, horseback riding, hiking and more. Newman explains that ranches, like Devil’s Thumb Ranch , Gros Ventre River Ranch and Tanque Verde Ranch , are a great way to get together in a secure environment but still have an outdoor adventure that leads to bonding with one another.

Safaris like Good Earth Tours , Australian Wildlife Journeys and Micato Safaris allow you to travel to a natural animal habitat. “Safaris are great, because you have a guide with you the whole time,” says Dziwulski. “The children get to see animals every day, which is an engaging, once-in-a-lifetime, memorable [experience]. But you have a guide with you all the time to cater to all of your needs.”

Group tours

Group tours are a popular option for skip-generational trips arranged by a travel agent. Road Scholar is a group tour company that focuses on experiential learning. It and Intrepid Travel offer specific trips for grandparents and grandchildren traveling together. Rick Steves’ Europe family tours are another group tour option. “We take care of all the planning and all the logistics, so you don’t have to think about what you need to plan ahead of time,” says Kelsey Knoedler Perri, director of public relations for Road Scholar.

Cheryl Maguire is a freelance writer whose work has been published in The New York Times , National Geographic , The Washington Post , The Boston Globe , Parents Magazine , Healthline and many other publications. She is a professional member of the American Society of Journalists and Authors.

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Trip Generation Appendices

Tgm appendices.

Click to download in Excel

Pass-By Data and Rate Tables

Time-of-Day Distribution - Truck

Time-of-Day Distribution - Vehicle

Trip Generation Data Plots - Modal

Click to download in PDF

000s - Port and Terminal - Modal Data Plots 1

200s - Residential - Modal Data Plots

300s - Lodging - Modal Data Plots

400s - Recreational - Modal Data Plots

500s - Institutional - Modal Data Plots

600s - Lodging - Modal Data Plots

700s - Office - Modal Data Plots

800s - Retail - Modal Data Plots

900s - Services - Modal Data Plots

Land Uses with Modal Data Plots

Trip Generation Data Plots - Truck

100s - Industrial - Truck Data Plots

200s - Residential - Truck Data Plots

300s - Lodging - Truck Data Plots

500s - Institutional - Truck Data Plots

600s - Medical - Truck Data Plots

700s - Office - Truck Data Plots

800s - Retail - Truck Data Plots

900s - Services - Truck Data Plots

Land Uses with Truck Data Plots

BREAKING: South Carolina beats Iowa to cap off an undefeated season

Your last-minute guide to Monday's total solar eclipse

Photo Illustration: The phases of a total solar eclipse

A total solar eclipse will cross North America on Monday , offering millions a rare opportunity to see afternoon skies temporarily darken as the moon blocks the face of the sun.

Tune into NBC News NOW as Lester Holt hosts a two-hour special at 2 p.m. ET Monday from Indianapolis Motor Speedway.

The eclipse's path fortuitously cuts across Mexico, 15 U.S. states and a small part of eastern Canada. In all other states in the continental U.S., viewers will be treated to a partial solar eclipse, with the moon appearing to take a bite out of the sun and obscuring part of its light.

Here’s everything you need to know about the rare celestial event.

What is a solar eclipse?

Solar eclipses occur when the sun, moon and Earth align. The moon passes between Earth and sun, temporarily blocking the sun’s light and casting a shadow on Earth.

A total solar eclipse is when the moon fully obscures the sun, whereas a partial solar eclipse means it blocks just a portion of the sun’s face.

Solar eclipses occur only with the new moon. Because the moon’s orbit around Earth is tilted, the three bodies don’t always line up in a way that creates an eclipse.

“Imagine if the moon’s orbit were in the plane of Earth’s orbit around the sun — if that were the case, then every new moon, you’d have a total solar eclipse and every full moon, you’d have a lunar eclipse,” Neil DeGrasse Tyson, director of the Hayden Planetarium at the American Museum of Natural History, told NBC News. “So, because things don’t always align, it lends to the rarity of the event and the specialness of the event.”

Where and when will the eclipse be visible?

This year’s eclipse will follow a slightly wider path over more populated areas of the continental U.S. than other total solar eclipses have in the recent past.

NASA estimates that 31.6 million people live within what’s known as the path of totality, where the total solar eclipse will be visible. An additional 150 million people live within 200 miles of the path, according to the agency.

The path travels through Texas, Oklahoma, Arkansas, Missouri, Illinois, Kentucky, Indiana, Ohio, Pennsylvania, New York, Vermont, New Hampshire and Maine. Tiny parts of Michigan and Tennessee will also be able to witness totality if conditions are clear.

After the eclipse crosses into Canada, it will pass over southern Ontario, Quebec, New Brunswick, Prince Edward Island and Cape Breton, at the eastern end of Nova Scotia.

Those outside the path of totality can still take part in the astronomical event by viewing a partial solar eclipse — visible throughout all 48 states of the contiguous U.S. — or a NASA livestream.

The timing, including how long totality lasts, depends on the location, but some spots will see the moon fully cover the sun for up to 4 minutes and 28 seconds.

Below is a list of timings for some cities along the path of totality, as provided by NASA . A number of other resources, including NationalEclipse.com and TimeandDate.com , can also help people plan.

Dallas: Partial eclipse begins at 12:23 p.m. CT and totality at 1:40 p.m.
Little Rock, Arkansas: Partial eclipse begins at 12:33 p.m. CT and totality at 1:51 p.m.
Cleveland: Partial eclipse begins at 1:59 p.m. ET and totality at 3:13 p.m.
Buffalo, New York: Partial eclipse begins at 2:04 p.m. ET and totality at 3:18 p.m.
Lancaster, New Hampshire: Partial eclipse begins at 2:16 p.m. ET and totality at 3:27 p.m.

This composite image of thirteen photographs shows the progression of a total solar eclipse

How to safely view a solar eclipse

It is never safe to gaze directly at the sun, even when it is partly or mostly covered by the moon. Special eclipse glasses or pinhole projectors are required to safely view solar eclipses and prevent eye damage. Failing to take the proper precautions can result in severe eye injury, according to NASA .

Eclipse glasses are thousands of times darker than normal sunglasses and specially made to enable wearers to look at the sun during these kinds of celestial events.

Sky-watchers should also never view any part of the sun through binoculars, telescopes or camera lenses unless they have specific solar filters attached. Eclipse glasses should not be used with these devices, as they will not provide adequate protection.

However, during the few minutes of totality, when the moon is fully blocking the sun, it is safe to look with the naked eye.

Beware of fake eclipse glasses. On legitimate pairs, the lenses should have a silver appearance on the front and be black on the inside. The manufacturer’s name and address should be clearly labeled, and they should not be torn or punctured. Check, as well, for the ISO logo and the code “IS 12312-2” printed on the inside.

If you don’t have eclipse glasses, you can make a homemade pinhole projector, which lets sunlight in through a small hole, focuses it and projects it onto a piece of paper, wall or other surface to create an image of the sun that is safe to look at.

All you need is two pieces of white cardboard or plain white paper, aluminum foil and a pin or thumbtack. Cut a 1- to 2-inch square or rectangle out of the center of a piece of white paper or cardboard. Tape aluminum foil over that cut-out shape, then use a pin or thumbtack to poke a tiny hole in the foil.

During the eclipse, place a second piece of white paper or cardboard on the ground as a screen and hold the projector with the foil facing up and your back to the sun. Adjusting how far you hold the projector from the second piece of paper will alter the size of the image on the makeshift screen.

What to look for while viewing the total solar eclipse

For people along the path of totality, there are some fun milestones to keep track of as the total solar eclipse unfolds.

As the eclipse progresses and the sun gets thinner in the sky, it will start to get eerily dark, according to Tyson.

The "diamond ring effect" is shown following totality of the solar eclipse at Palm Cove in Australia's Tropical North Queensland in 2012.

When the last beams of sunlight are about to become obscured, look out for the “diamond ring effect”: The sun’s atmosphere will appear as an illuminated halo, and the last light still visible will look like the diamond of a giant ring.

As the sunlight decreases even further, an effect known as Baily’s beads will be created by the moon’s rugged terrain. Tiny “beads” of light will be visible for only a few seconds around the dark moon, as the last bits of sunlight peer through the moon’s mountains and valleys.

When the moon is fully blocking the sun, it is safe to remove eclipse glasses and look at the total solar eclipse with the naked eye.

The Bailey's Beads effect is seen as the moon makes its final move over the sun during the total solar eclipse on Monday, August 21, 2017 above Madras, Oregon.

Some lucky sky-watchers may even catch a glimpse of a comet .

Comet 12P/Pons-Brooks — nicknamed the “ devil comet ” because an eruption last year left it with two distinct trails of gas and ice in the shape of devil horns — is currently visible from the Northern Hemisphere as it swings through the inner solar system.

The comet can be seen in the early evenings by gazing toward the west-northwest horizon. During the eclipse, when skies darken during totality, it may be possible to see the comet near Jupiter, but its visibility will depend on whether it’s in the middle of an outburst and thus brighter than normal.

Most likely, all eyes will be on the alignment of the moon and sun.

“Most people won’t even notice,” Tyson said. “But if you know to look, it’s there.”

When is the next solar eclipse?

The next total solar eclipse will be in 2026, but it will mostly pass over the Arctic Ocean, with some visibility in Greenland, Iceland, Portugal and northern Spain. In 2027, a total solar eclipse will be visible in Spain and a swath of northern Africa.

The next total solar eclipse visible from North America will be in 2033, but only over Alaska. Then in 2044, a total solar eclipse will cross Montana, North Dakota, South Dakota, parts of Canada and Greenland.

The next total solar eclipse to cross the continental U.S. coast-to-coast in will occur in 2045. The path of totality for that eclipse will cut through California, Nevada, Utah, Colorado, New Mexico, Oklahoma, Kansas, Texas, Arkansas, Missouri, Mississippi, Louisiana, Alabama, Georgia and Florida.

Denise Chow is a reporter for NBC News Science focused on general science and climate change.

Lucas Thompson is a content producer for the NBC News Climate Unit.

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Trip Generation and Trip Distribution
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VIDEO

Trip Generation Example
Trip Generation Model
Lec-20_Trip Generation-Linear Regression Analysis
Lec-32_Modal Split Analysis
Lecture 02 Trip Generation and Trip Distribution
Mod-03 Lec-10 Trip Generation Analysis Contd

COMMENTS

3.4: Trip Generation
3.4: Trip Generation. Trip Generation is the first step in the conventional four-step transportation forecasting process (followed by Destination Choice, Mode Choice, and Route Choice), widely used for forecasting travel demands. It predicts the number of trips originating in or destined for a particular traffic analysis zone.
Trip generation
Trip generation is the first step in the conventional four-step transportation forecasting process used for forecasting ... this many acres of commercial land use, that many acres of public open space, etc., in the zone. The acres of each use type are multiplied by the ring specific destination rates. The result is summed to yield the zone's ...
10 First Step of Four Step Modeling (Trip Generation)
We can estimate trip generation rates by calculating the average weekday peak-hour trips generated by a particular land use. The trip generation rate for each land-use type is the total number of weekday peak-hour trips. The Institute of Transportation Engineers publishes this rate based on field observations in the Trip Generation Manual (ITE ...
Trip and Parking Generation
The trip generation database includes both vehicle and person trip generation for urban, suburban and rural settings. A Trip Generation web app—ITETripGen allows electronic access to the entire dataset with numerous filtering capabilities including site setting, geographic location, age of data, development size, and trip type.
How to Determine Trip Generation Types
Pass-By and Diverted Number of Trips. Use either local data or ITE data to determine a percentage of the reduced trip generation that is pass-by or diverted. Similar to the ITE Trip Generation data, both pass-by and diverted trip percentages are available by average rate or an equation for many land uses. Use this percentage to calculate the ...
Trip generation in Transport Planning
By assigning suitable values to the independent variables of the regression equations forecasts can be made of the future trip ends for zones by either method. Trip distribution:Trip generation estimates the number and types of trips originating and terminating in zones. Trip distribution is the process of computing the number of trips between ...
Fundamentals of Transportation/Trip Generation
Trip Generation is the first step in the conventional four-step transportation forecasting process (followed by Destination Choice, Mode Choice, and Route Choice ), widely used for forecasting travel demands. It predicts the number of trips originating in or destined for a particular traffic analysis zone. Every trip has two ends, and we need ...
A comprehensive review of trip generation models based on land use
Significant variations in trip rates were observed across different land use types indicating the necessity of incorporating land use characteristics in trip generation modelling. Several other studies have tried to explore the development of trip generation models in the context of developing countries ( Srinivasan and Rogers, 2005 , Goel and ...
Trip and Parking Generation Resources
This updated manual follows the lead of the modernized, updated, and expanded Trip Generation Manual, 10th Edition. The analyses in Parking Generation will differentiate the levels of parking demand observed at rural, general urban/suburban, dense multi-use urban, and center city core sites. Jan 31, 2019.
Trip Generation Handbook
Trip Generation Handbook. ... They enable the analyst to estimate vehicle or person trip generation of individual land use types by direction, inbound and outbound, and estimate peak hour vehicle or person trip generation using commonly available independent variables. (2) The recommended methods can be used now for all land uses and contexts ...
(PDF) An Overview of Trip Generation
Abstract. Two different approaches are used to collect trip generation data that form the basis of both site analysis--for example, ITE trip generation rates--and longer term forecasting--for ...
Trip Generation Handbook
Trip Generation Handbook. The principal objectives of Trip Generation Handbook, 3rd Edition (the Handbook) are to provide recommendations for proper techniques for estimating trip generation, both person and vehicle, for potential development sites in urban, suburban, and rural settings; standardization of trip generation data collection efforts; and ethics and objectivity in the use of Trip ...
Trip Generation Manual, 11th Edition
Trip Generation Manual, 11th Edition. This new edition of the Trip Generation Manual enhances the 10th edition's modernized content, data set, and contemporary delivery - making it an invaluable resource. The 11th edition features: (1) All the latest multimodal trip generation data for urban, suburban and rural applications, (2) Reclassified ...
Introduction to Transportation Modeling: Travel Demand Modeling and
Trip Generation. Trip generation is the first step in the FSM model. This step defines the magnitude of daily travel in the study area for different trip purposes. It will also provide us with an estimate of the total trips to and from each zone, creating a trip production and attraction matrix for each trip's purpose. Trip purposes are ...
PDF ber 2021 O Oct
the trip generation land use plots. We have taken this a step further with the 11th Edition, creating a single source for all your trip generation resources. In addition to digital access to all land use plots and descriptions, TripGen11 provides an electronic copy of the TGM Desk Reference and the ITE Trip Generation Handbook. You can find ...
Trip Generation
Trip Generation Introduction. Trip generation is the first step in the traditional, sequential four step process of transportation modeling. It establishes a relationship between land use, socioeconomic and demographic data and trip productions and attractions. ... Two trip types are shown on the screen, but several types could be shown.
Travel Demand Forecasting: Parameters and Techniques
Trip generation models require some explanatory variables that are related to trip-making behavior and some functions that estimate the number of trips based on these explanatory variables. ... and employment by type. The output of trip generation is trip productions and attractions by traffic analysis zone and by purpose. Trip distribution ...
Trip Generation Analysis
Trip Generation Analysis. The following excerpt was taken from the Transportation Planning Handbook published in 1992 by the Institute of Transportation Engineers (pp. 108-112). Trip Generation Models. (p. 110) There are two kinds of trip generation models: production models and attraction models. Trip production models estimate the number of ...
(PDF) CREATING TRIP GENERATION MODELS FOR UNPLANNED CITIES
Trip generation is the first and biggest challenge in transportation modeling process. This stage is used to predict all generated trips including those starting inside and outside the study area.
TRICS® System
TRICS® is the system of trip generation analysis for the UK and Ireland. First launched in 1989, it is an integral and essential part of the Transport Assessment process, and through continuous investment and development it has expanded into a comprehensive database of traffic and multi-modal transport surveys, covering a wide range of development types.
Trip Generation
Person-type based trips. The travel behaviour of an individual is mainly dependent on its Socio-Economic attributes. ... Trip generation study typically involves the application of residential trip production which contains variable that defines the demographic makeup of zonal population and trip attraction that captures the activity of non ...
Trip Generation Example
Institute of Transportation Engineers (ITE) is the primary source of trip generation data in the Trip Generation Manual.Land use types include residential, l...
Skip-Generation Trips: What They Are and How to Plan One
We spoke to travel experts to find the best tips and types of travel for your skip-generation trip. 1. Talking with your child about traveling without them. "The initial conversations have to be between the grandparent and the parents, without the grandchild involved, and just make sure that the parents are comfortable with it," says ...
Trip Generation
Trip Generation Data Plots - Modal. Click to download in PDF. 000s - Port and Terminal - Modal Data Plots 1. 200s - Residential - Modal Data Plots. 300s - Lodging - Modal Data Plots. 400s - Recreational - Modal Data Plots. 500s - Institutional - Modal Data Plots. 600s - Lodging - Modal Data Plots. 700s - Office - Modal Data Plots.
Solar eclipse 2024 explained: Times it's visible, path of totality, why
The eclipse's path fortuitously cuts across Mexico, 15 U.S. states and a small part of eastern Canada. In all other states in the continental U.S., viewers will be treated to a partial solar ...

3.4: Trip Generation

Specifying Models

Estimating Models

Non-home-end

Normalization

Sample Problems

Abbreviations

External Exercises

Traffic Impact Study Improvements: Part 5 – When is a Trip Not a Trip?

Trip generation

How to estimate trip generation ?

About The Author

Fundamentals of Transportation/Trip Generation

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Non-home-end [ edit | edit source ]

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Trip Generation Manual, 11th Edition

Subject/Index Terms

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9 Introduction to Transportation Modeling: Travel Demand Modeling and Data Collection

Introduction

9.2 Four-step Model

Trip Generation

Trip Distribution

Mode choice

Trip assignment

Model improvement

A Simple Example of the FSM model

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Travel Demand Forecasting: Parameters and Techniques (2012)

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What is TRICS and how does it work?

What is TRICS®?

TRICS® contains...

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What is the best way to use TRICS®

Using the System

Supply and Commission of TRICS® Data

How to use TRICS®

System Navigation

Trip Rate Calculations

What are trip rates?

How are they calculated?

Stage 1: Obtaining average (mean) trip rate parameter figures

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Making the Most of Skip-Generational Trips

1. Talking with your child about traveling without them

2. Explaining expectations during the trip

3. Consider activity levels, mobility needs and downtime

4. Consider these trips for skip-generation travel

Trip Generation Appendices

Trip Generation Data Plots - Modal

Trip Generation Data Plots - Truck

Your last-minute guide to Monday's total solar eclipse

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