Appendix JTravel Demand Modelling Process J.1 Trip Generation

The first step in travel forecasting is trip generation. In this step, information from land use, population, and economic forecasts are used to estimate how many trips will be made to and from each zone.

Types of Most Commonly Used Trip Generation Models

Regression models: equations such as:

Total trips = 1.1*population,

Home-based work trips = 1.5*total employment, etc;

Cross-classification tables based on: household size, number of workers, income class, vehicle ownership, etc.;

Special generator models: used for generators such as airports, colleges, amusement parks, green spaces, etc.;

Separate rates for peak and off-peak trips.

Assumptions and limitations:

Pedestrian and bicycle trips are usually excluded from trip generation models.

Commercial vehicles need to be generated separately.

Care should be taken before interchanging vehicle trip rates with person trip rates.

Compare trips per household to the regional average. There should be a valid explanation if numbers are too high or too low.

Review trips per household by purpose.

Ensure that trip productions and trip attractions balance well. Check trip rates and adjust if they do not balance

Create GIS color-coded maps of productions and attractions to review for errors. Use special generators for facilities such as schools, hospitals, malls, and recreational facilities such as parks, playgrounds, etc.

Make model external trips and visitor trips using external cordon and visitor surveys.

Make sure Commercial Vehicle and Freight Travel is included in the model

The trip distribution model estimates number of trips travelled from one zone to another. Trip generation only finds the number of trips that begin or end at a particular zone. The process of trip distribution links the trip ends to form an origin-destination pattern. Trip distribution is used to represent the process of destination choice (i.e. "I need to go shopping, but where should I go to meet my shopping needs?").

The most commonly used procedure for trip distribution is called the gravity model. The gravity model takes the trips produced at one zone and distributes to other zones based on both the size of the other zones (as measured by their trip attractions) and the composite cost (travel time, distance or cost for all modes available) to other zones. A zone with many trip attractions (say, a large shopping center) will

receive a greater number of distributed trips than one with few attractions (a small shopping center). The mathematical formula for a gravity model is shown below:

Where,

$T _ { j } { \overline { { \overline { { \phantom { . } } } } } } ^ { \mathrm { { { } { { { { { \mathrm { { { \scriptsize ~ } } } } } } } } } } }$ the number of trips from zone i to zone $j ,$

$P _ { i }$ = the number of trip productions in zone

$A _ { j } { = }$ the number of trip attractions in zone j,

$F _ { i j } { = }$ the friction factor relating the spatial separation between zone i and zone j, and

$K _ { i j } { = }$ an optional trip-distribution adjustment factor for interchanges between zone i and zone j.

Distance to possible destinations is the other factor used in the gravity model. The number of trips to a given destination decreases as the distance to the destination increases (it is inversely proportional). This is represented in a factor called friction factor. It is inversely proportional to the distance or travel time between two zones. Friction factors are used to calibrate trip lengths from the model to observed data. The most common formulation of friction factors is through a gamma function as shown below:

Where,

$\mathsf { F } _ { \widehat { \mathbb { Y } } }$ = the friction factor between zones i and j,

a, b, and c = model coefficients; both b and c should, in most cases, be negative; a is a scaling factor and can be varied without changing the distribution,

$t _ { \mathrm { i } } { = }$ the travel time between zones i and j, and

e = the base of the natural logarithms.

Validation and Reasonableness Checking for trip distribution

Compare trip length frequency from model and survey data.

Do this for every purpose

Do this for distance and time

If different, adjust friction factors in the model

Maximum of 5-10% difference in observed and modelled.

Compare trip patterns from the model to data from O-D Surveys.

Aggregate data into districts and compare trips

Look at north-south and east-west trips