Transportation Forecasting

Trip Generation

The Four Step Model

• Trip GenerationEstimates the number of trips from given origins and destinations

• Trip DistributionDetermines the destination for each trip from a given origin

• Mode ChoiceDetermines the mode choice for each trip

• Route AssignmentDetermines the specific route for each trip

Trip GenerationTrip Generation model is used to estimate the number of person-

trips that will begin or end in a given traffic analysis zone (TAZ)

The unit of analysis for traffic generation is the TAZ

1

23

4

5

6

87

Trip GenerationDeveloping and Using the Model

Calibrated Model

Relating trip making to socio-economic and land use data

Estimated Target year

socio-economic,land use data

Predict Target year No. of Trips

Survey Base YearSocio-economic, land use

and Trip making

Trip GenerationForm of the Model

The trip generation model typically can take the form of

No. of trips = Function (population, income, auto ownership rates)

The model is developed and calibrated using the BASE year data

Trip GenerationTravel Survey

Trip Generation models are often developed from travel surveys.

These surveys are used to determine the trip making pattern for a sampling of households in the area.

This trip making pattern is then related to land use and socioeconomic factors that are considered to affect travel patterns

Common socioeconomic factors considered include population, income, and auto ownership rates

Trip GenerationTrip Purpose

Often separate predictions are mode for different type of trips since travel behavior depends on trip purpose

In other words different models must be developed for each trip type

The category of trip types commonly used include• Work trips• School trips• Shopping trips• Recreational trips

Trip GenerationExample of a Trip Generation Model

One way of presenting the trip generation model developed from a survey is as a cross-classification table

Trip GenerationThe Survey and The Model

Calibrated Model

Relating trip making to socio-economic and land use data

Survey Base YearSocio-economic, land use

And Trip making

Trip GenerationTrip Rates

Total Home-Based-Non-Work Trip Rates

Persons per Household

Type of Area

Vehicles per HH

1 2,3 4 5+

High Density 0

1

2+

Low Density 0

1

2+

Trip GenerationTrip Rates

Total Home-Based-Non-Work Trip Rates

Persons per Household

Type of Area

Vehicles per HH

1 2,3 4 5+

High Density 0 0.6

1 1.5

2+ 1.8

Low Density 0

1

2+

Trip GenerationTrip Rates

Total Home-Based-Non-Work Trip Rates

Persons per Household

Type of Area

Vehicles per HH

1 2,3 4 5+

High Density 0 0.6 7.0

1 1.5 7.9

2+ 1.8 8.3

Low Density 0

1

2+

Trip GenerationTrip Rates

Total Home-Based-Non-Work Trip Rates

Persons per Household

Type of Area

Vehicles per HH

1 2,3 4 5+

High Density 0 0.6 2.1 4.6 7.0

1 1.5 3.0 5.5 7.9

2+ 1.8 3.4 5.9 8.3

Low Density 0

1

2+

Trip GenerationTrip Rates

Total Home-Based-Non-Work Trip Rates

Persons per Household

Type of Area

Vehicles per HH

1 2,3 4 5+

High Density 0 0.6 2.1 4.6 7.0

1 1.5 3.0 5.5 7.9

2+ 1.8 3.4 5.9 8.3

Low Density 0 1.0 2.5 5.0 7.4

1 1.9 3.5 6.0 8.4

2+ 2.3 3.9 6.4 9.0

Trip GenerationEstimating Target Year Data

Calibrated Model

Relating trip making to socio-economic and land use data

Estimated Target year

socio-economic,land use data

Trip GenerationTarget Year Data

Number of Households in Target Year

Persons per Household

Type of Area

Vehicles per HH

1 2,3 4 5+

High Density 0

1

2+

Low Density 0

1

2+

Trip GenerationTarget Year Data

Number of Households in Target Year

Persons per Household

Type of Area

Vehicles per HH

1 2,3 4 5+

High Density 0 100 200 100 100

1 200 300 200 100

2+ 100 200 100 200

Low Density 0

1

2+

Trip GenerationTarget Year Data

Number of Households in Target Year

Persons per Household

Type of Area

Vehicles per HH

1 2,3 4 5+

High Density 0 100 200 100 100

1 200 300 200 100

2+ 100 200 100 200

Low Density 0 50 100 100 100

1 100 200 100 100

2+ 100 100 100 10

Trip GenerationPredicting Number of Trips

Calibrated Model

Relating trip making to socio-economic and land use data

Estimated Target year

socio-economic,land use data

Predict Target year No. of Trips

Number of Trips in Target Year for Each HH Type

Persons per Household

Type of Area

Vehicles per HH

1 2,3 4 5+

High Density 0

1

2+

Low Density 0

1

2+

Trip GenerationPredicting Number of Trips

Number of Trips = trip rate*no. of HH = 0.6 * 100 = 60

60

Trip GenerationPredicting Number of Trips

Number of Trips in Target Year for Each HH Type

Persons per Household

Type of Area

Vehicles per HH

1 2,3 4 5+

High Density 0 60 420 460 700

1 300 900 1100

790

2+ 180 680 590 1660

Low Density 0 50 250 500 740

1 190 700 600 840

2+ 230 390 640 90

Trip GenerationPlanning for the Future and Uncertainties

Earlier we talked about the uncertainties associated with making prediction for the future and the importance of not treating predictions as if they are set in stone but rather as a guide to help in decision making

In considering the ‘trip generation’ process it is important to understand some potential sources of uncertainties

Trip GenerationSources of Uncertainties in Predicting

Number of Trips

Significant errors can creep into the trip generation process in a number of places including

Errors in the survey Who is surveyed, how well was the survey constructed,

did we consider all important parameters?

Errors in the prediction of future demographicsWill the population grew, what will be the make up of the

population

Errors in how well the model can actually reflect the future

Will the land use change, will transportation change, will technology change, will peoples attitudes change

Trip GenerationEffect of Changes in Land Use

Changes in Land use and the type of transportation provided can have a huge impact on travel

But the trip generation process typically assume that this factor is constant over the period of the study

Trip GenerationDemographics and Trip Making Factors affected

by Land Use

The land use pattern and transportation type may affect

Car ownership rates Household size and composition Number of daily trips Mode of trips Length of trips