Fundamentals of Transportation/Trip Generation

Trip Generation

What do people do all day?

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

In the main trip generation analysis is focused on residences, and that trip generation is thought of as a function of the social and economic attributes of households. At the level of the traffic analysis zone, the language is that of land uses "producing" or generating trips. Zones are also destinations of trips, trip attractors. The analysis of attractors focuses on nonresidential land uses.

Activities

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. We might say trips are produced at an origin and attracted to 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

Trip Purpose	Males	Females	Total
Work	4008	3691	7691
Work related	1325	698	2023
Attending school	495	465	960
Other school activities	108	134	242
Childcare, daycare, after school care	111	115	226
Quickstop	45	51	96
Shopping	2972	4347	7319
Visit friends or relatives	856	1086	1942
Personal business	3174	3928	7102
Eat meal outside of home	1465	1754	3219
Entertainment, recreation, fitness	1394	1399	2793
Civic or religious	307	462	769
Pick up or drop off passengers	1612	2490	4102
With another person at their activities	64	48	112
At home activities	288	384	672

Some observations: men and women behave differently, 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), 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 produced by zone?

The number of trips produced or attracted 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 are the relevant variables.

Home-end

The total number of trips leaving (produced from) or returning (attracted to) to homes in a zone may be described as a function of:

$T_h=f(housingunits,householdsize,age,income,accessibility,vehicleownership).$

Work-end

$T_w=f(jobs(squarefeetofspacebytype,occupancyrate))$

Shop-end

$T_s=f(numberofretailworkers,typeofretail,squarefoot,location,competition)$

Input Data

A forecasting activity, 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) Every individual’s trip-making pattern is different

Home-end

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.

Figure 1 shows a typical example of how trips vary by age in both single-family and multi-family residence types.

Is reality this volatile?

Figure 2 shows a moving average.

Non-home-end

Non-Home-End Trip Generation

The trip generation rates for both “work” and “other” trip ends were developed using Ordinary Least Squares (OLS) regression 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:

$T_i=a_1{E}_{off,i}+a_2{E}_{oth,i}+a_3{E}_{ret,i}$

Where:

• $T_i$ - Person trips attracted per worker in the ith zone
• $E_{off,i}$ - 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

The number of trips produced (at home) from home to work must equal the number of trips attracted (at work). Two distinct models may give two results. Either assume one model or the other is correct and adjust the second, or split the difference. It is necessary to assure 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:

$T{\text{'}}_j=T_j\frac{\sum _{i=1}^I{T}_i}{\sum _{j=1}^J{T}_j}$

Example Problems: Applying Models

1. Fundamentals of Transportation/Trip Generation Problem

Further Reading

• Trip Generation article on wikipedia

Jargon

• 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)

Variables

• Ti - Person trips originating in Zone i
• Tj - Person Trips destined for Zone j
• Ti’ - Normalized Person trips originating in Zone i
• Tj’ - Normalized Person Trips destined for Zone j
• Th - Person trips generated at home end (typically morning origins, afternoon destinations)
• Tw - Person trips generated at work end (typically afternoon origins, morning destinations)
• Ts - Person trips generated at shop end
• Hi - Number of Households in Zone i
• Eoff,i - office employment in the ith zone
• Eret,i - retail employment in the ith zone
• Eoth,i - other employment in the ith zone
• Bn- model coefficients

Activity Analysis

Frequency- How many times the trip is made per day

Scheduling-the order in which the trips are made

Activities-home, work, shop other (Non-Discretionary). Schools, church, visit friends, recreation, visit doctor (Discretionary).

Patterns – HWH, HWSH, and HWHSH.

These are a function of sex, age, employment, status, income, auto availability. Important things to note in household study are the household size (more predictable), household structure (less predictable). Location/accessibility studies involve feedback. Dwelling unit types are obtained from the land use pattern and are an indicator of the income, race, household structure. They are single units and multi-family types.

Time of day: The time of day can be derived from the pattern and duration of activities. Scheduling models give the pattern of activities and not how long each activity takes place.

In a trip generation framework the peak hour factor used is a constant and is a function of congestion.