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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 ???