Trip Generation

Gopal R. Patil Indian Institute of Technology Bombay,

Mumbai

CE 415: Transportation Engineering II

Traffic Analysis Zone (TAZ)

• The city or the region under consideration for demand model is divided into smaller areas called Traffic Analysis Zones (TAZs)

• Guidelines on Creating TAZs – Homogeneous socio-economic characteristics – Minimum intra-zonal trips – Physical, political, and historical boundaries observed – Use census tract boundaries whenever possible

• Trip Generation is the first step in classic four-stage Demand Models

• Answers to a question, how many trips produced by and attracted to a Traffic Analysis Zone?

Trip Generation

• A Trip in transport modeling is a travel from an origin to a destination

• Home Based Trip: One of the trip ends is home (place of residence)

Example: A trip from home to office

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

Example: A trip from office to Shopping Mall

Basic Definitions

Trip Productions and Attractions

• Trip Production: Home end of a home-based (HB) trip or the origin of non-home-based (NHB) trip

• Trip Attraction: Non-home end of a HB trip or the destination of a NHB trip

Residential Area

Non-residential Area

Non-residential Area

Non-residential Area

Production

Production

Production

Attraction

Attraction

Attraction

Attraction

Production

Home-based Trips

Non-home-based Trips

= Origin

= Destination

Trip Productions and Attractions

Classification of Trips

• Trip Purpose – Work – School – Shopping – Social and recreation – other

Compulsory Trips

Discretionary Trips

Classification of Trips

• Time of Day – Peak hour – Off-peak hour

• Person Type – Income (different income levels; e.g., low, middle,

high) – Car ownership (0, 1, 2, 3 or more cars) – Household size and structure

Typical Trip Chain

Home

Market

Work

1.Home-based work trip

2.NonHome-based shopping trip

3. Home-based shopping trip

1—2—3 :Tour or Trip Chain (home-based)

• Trip Production – No of workers in a household – No of Students – Household size and composition – The household income – Some proxy of income such as number of cars, etc. – Accessibility

• Trip Attraction – Land use – Commercial space – Number of employees – accessibility

Factors Affecting Trip Generation

• Trip Rates • Growth factor models

– Based on extrapolation from existing condition • Regression Models

– Explanatory Variables are used to predict trip generation rates, usually by Multiple Regression

• Cross - Classification / Category Analysis – Average trip generation rates are associated with different

trip generators or land uses as a function of generator or land use attributes

• Models may be TAZ, Household, or Person - Based

Trip Generation Models

Trip Rates

• Trips are obtained from trip rate tables or charts prepared using historical data of different places

• For example, Trip Generation handbook prepared by Institute of transportation engineers (ITE) using data from the USA

Trip Rates

• Easy and minimal data requirements (+) • Can result in different values of trip generation for

different known factors (-)

Number of Persons

Trip

s

Number of Vehicles

Trip

s

• Based on extrapolation from existing condition 𝑇𝑖 = 𝐹𝑖𝑡𝑖

– 𝑇𝑖 is the number of future trips in zone 𝑖 – 𝑡𝑖 is the number of current trips in zone 𝑖 – 𝐹𝑖 is a growth factor

• Not easy to estimate 𝐹𝑖 • Usually

𝐹𝑖 =𝑓 𝑝𝑝𝑝𝑝𝑝𝑝𝑡𝑖𝑝𝑝, 𝑖𝑝𝑖𝑝𝑖𝑖, 𝑖𝑝𝑐𝑝𝑐𝑝𝑖𝑐𝑐𝑐𝑖𝑝 𝑓𝑓𝑓𝑓𝑓𝑓

𝑓 𝑝𝑝𝑝𝑝𝑝𝑝𝑡𝑖𝑝𝑝, 𝑖𝑝𝑖𝑝𝑖𝑖, 𝑖𝑝𝑐𝑝𝑐𝑝𝑖𝑐𝑐𝑐𝑖𝑝 𝑏𝑏𝑏𝑓

Growth Factor Model

Growth Factor Model

In a simple form

𝐹𝑖 =𝑃𝐹

𝑃𝐵

𝛾

.𝐼𝐹

𝐼𝐵

𝜑

𝑃 : population 𝐼 : Income

• Easy method but very simplistic • Usually used to estimate trips in external zones

• Statistical methodology that utilizes the relation between two or more quantitative variables so that one variable can be predicated from others

• The general form of a trip generation model is • 𝑇𝑖 = 𝑓(𝑥1, 𝑥2, … , 𝑥𝑘) • A multiple linear regression model will of the

following form: 𝑇𝑖 = 𝑝0 + 𝑏1𝑥1 + 𝑏2𝑥2+, … , +𝑏𝑗𝑥𝑗+, … .

Where, 𝑥1, 𝑥2, … , 𝑥𝑗 are predictor variable

Regression Method

• Trips produced or attracted in a zone is a function of socioeconomic characteristics of households in that zone

• Two forms of variables: – Aggregate, that is, zonal total (eg. Zonal population, total

number of cars, etc) • Heteroscedasticity (larger zones have larger variance)

– Zonal average (avg. HH income, avg. HH car ownership, etc)

Zonal Based Regression

Zonal Based Regression

• Can only explain the variation in trip making behavior between zones (zones need to be homogeneous and small)

• Model should not be developed by mixing aggregate and average variables

• Models with very high intercepts may be rejected

• Trip per household is a function of household socioeconomic variables (HH size, HH income, HH cars)

• Zonal characteristics do not influence the trip predications

• Intra zonal trips are not ignored • More accurate and better representation than the

zonal based model

House Hold Based Regression Model

House Hold Based Regression Model

• Eg. 𝑌 = 0.84 + 1.21𝑥1 + 0.9𝑥2 𝑌 = trips per HH 𝑥1= number of workers in the HH 𝑥2 = number or cars

• Zone based models are useful for trip attraction whereas House hold models are popular for trip productions

• Some factors that influence trip generation can have non linear relationship

0

1

2

3

4

5

6

0 1 2 3 4 5 6

Trip

s per

HH

Number of persons in HH

1 car 1 worker

Non-linear Relationship

Couples without kids

Couples with one kid

Couples with two kids and one adult

Non-linear Relationship

• Two approaches to linearize the non-linear relationship – Variable transformation (logarithm, power, etc) – Use of dummy variables: The independent variables

with non-linear relationship is divided into several intervals

Use of Dummy Variables

• 𝑌 = 𝑝0 + 𝑏1𝑥1 + 𝑏2𝑥2 𝑥1= number of workers in the HH 𝑥2 = number or cars

• The variable 𝑥2 can be divided into car ownership of 0, 1, and 2 or more (needs two dummy variables)

• The resulting model with two dummy variables • 𝑌 = 𝑝0 + 𝑏1𝑥1 + 𝑏2′ 𝑧1 + b2′′z2 𝑧1= 1 if HH with one car; 0 otherwise 𝑧2= 1 if HH with two or more car; 0 otherwise

Use of Dummy Variables

0

1

2

3

4

5

6

7

8

0 1 2 3 4 5

Trip

s per

HH

Number of Workers in HH

0 cars

1 cars

2 or more cars

Aggregation (Zonal Total)

• Zonal models: Trips at zonal level are readily available

• Household models – Linear Models: aggregation is straight forward – Consider – Trips for zone Number of households in zone Average number of workers per HH Average number of cars per HH

Aggregation (Zonal Total)

• Model with Dummy Variables • Model, 𝑌 = 𝑝0 + 𝑏1𝑥1 + 𝑏2′ 𝑧1 + b2′′z2 • Aggregation 𝑇𝑖 = 𝐻𝑖(𝑝0+𝑏1�̅�1) + 𝑏2′𝐻1𝑖 + 𝑏2′𝐻2𝑖 𝐻1𝑖 = number of HH with one car 𝐻2𝑖 = number of HH with two or more cars

• Conceptualize population segment according to key trip generation variables (car ownership, income and household structure )

• All households are assigned a household group • If four household sizes (1, 2, 3, and 4 or more) and

three car ownership groups (0, 1, and 2 or more), we have 12 household groups (cells)

• The trip rates are estimated for each group (cell) • Trip generation rates for household are assumed to

remain reasonably stable over time

Category Analysis (Cross-classification)

Pros and Cons

• Pros – Grouping is independent of the zoning – No prior assumption about the relationship between

response and predictor variable (Linear, monotonic, etc)

• Cons – Extrapolation not possible – Large sample size required – Grouping of variables is arbitrary

An Example

HH Size

Car Ownership

0 1 2 or more

1 0.12 0.94

2 or 3 0.60 1.38 2.16

4 1.14 1.74 2.6

5 1.02 1.69 2.60

HH Size

Car Ownership

0 1 2 or more

1 50 200

2 or 3 30 150 450

4 20 100 600

5 5 50 300

Sample Rates using Cross-classification Number of Households

Total number of trips produced in the zone , T = 0.12 x 50 + 0.94 x 200 + ….. + 2.60 x 300 = 992 trips per day

Thank You!

Questions ??? Comments ???