Appendix JTravel Demand Modelling Process J.1 Trip Generation

URDPFI Guidelines 2014 Volume II-B: Appendices

2014 · PDF page 125

Hover any section to copy its link or text, ask Copilot about it, or bookmark it.

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.

Validation and Reasonableness Checking for trip generation

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

J.2 Trip Distribution

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:

T _ {i j} = P _ {i} \left(\frac {A _ {j} F _ {i j} K _ {i j}}{\sum_ {k = 1} ^ {\text {zones}} A _ {k} F _ {i j} K _ {i k}}\right)

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:

F _ {i j} = a X t X e ^ {c x t _ {e}}

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

J.3 Modal Split

Mode choice is one of the most critical parts of the demand modelling process. It is the step where trips between a given origin and destination are split into trips using available modes such as two-wheeler, car, private vehicle, public transport, etc. Calculations are conducted that compare the attractiveness of travel by different modes to determine their relative usage. All proposals to improve public transport or to change the ease of using private vehicles are passed through the mode split/private vehicle occupancy process as part of their assessment and evaluation. It is important to understand what factors are used and how the process is conducted in order to plan, design, and implement new systems of transportation. The most common mathematical formulation of a mode choice model is a logit model and is as follows:

P _ {i} = \frac {e ^ {u _ {i}}}{\sum_ {i = 1} ^ {k} e ^ {u _ {i}}}

Where,

$\mathsf { P } _ { \mathrm { i } } =$ the probability of a traveller choosing mode i,

$\mathbf { u } _ { \mathrm { i } } = \mathbf { a }$ linear function of the attributes of mode i that describe its attractiveness, also known as the utility of mode i, and

$\sum _ { i = 1 } ^ { k } e ^ { u i }$ = the summation of the linear functions of the attributes of all the alternatives, k, for which a choice is available

The utility function is calculated based on the following equation:

u _ {i} = a _ {i} + b _ {i} + I V T T _ {i} + c _ {i} \times O V T T _ {i} + d _ {i} \times C O S T _ {i}

Where,

IVTT_i = the in-vehicle travel times for mode i,

OVTT_i = set of variables measuring the out-of-vehicle travel times for mode i—walk, wait, and transfer times—may all be kept separate or combined, depending on the calibrated structure of the model,

COST_i = the cost of mode i,

a_i = mode-specific coefficient (constant) to account for mode bias not measurable with the level of service variables,

b_i= coefficient for the IVTT variables of mode

c_i = a set of coefficients for OVTT variables of mode i, and

d_i= coefficient for COST variable of mode i

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:

T _ {i j} = P _ {i} \left(\frac {A _ {j} F _ {i j} K _ {i j}}{\sum_ {k = 1} ^ {\text {zones}} A _ {k} F _ {i j} K _ {i k}}\right)

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.

F _ {i j} = a X t X e ^ {c x t _ {e}}

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