Transport Planning: Trip Generation
Travel-Demand Forecasting Process
Trip Generation
Trip Distribution
Mode Split
Transportation
Network & Service
Attributes
Link & O-D Flows,
Times, Costs, etc.
Trip Assignment
Population & Employment Forecasts
General Framework of 4-Step Models
How many trips
will be made?
Trip Generation
Trip Distribution
Mode Split
Trip Assignment
I
Oi
J
Dj
Trip Generation
I J
Trip Distribution
Tij
I J Mode Split
Tij,auto
Tij,transit
I
J
Trip Assignment
-- path of flow Tij,auto
through the auto
network
General Framework of 4-Step Models
Travel-Demand Forecasting Process
Demand for added capacity and parking facilities is not uniformly distributed throughout urban areas
Dependent on type of land use in each zone
residential
commercial
Industrial, etc.
Dependent on intensity of land use in each zone
residential density
workers per acre
shopping floor space, etc.
Travel-Demand Forecasts
Trip generation models were postulated, calibrated and validated to
relate trip-producing capability of residential areas and trip-attracting
potential of various non-residential types of land-use.
Components of Mathematical Models
Components of Mathematical Models
Components of Mathematical Models
Map is defined a priori
Zone boundaries defined
Based on survey data
Zone land use quantified
1st: Define Network
Map is defined a priori
Zone boundaries defined
Based on survey data
Zone land use quantified
Generate Number of Trips:
TO each zone (Attractions)
FROM each zone (Productions)
Function of Land Use and socio-demographics in each zone
2nd: Generate Travel Demands
Trip generation is a function of
land use activity
Industry Hospitals Shopping centers
Residential zones Schools ..
Workforce
Measures of land use activity
Activity Measure
Employment centre Number of jobs
Residential area
Education centre
Hospital
Retail centre
Industrial estate
Farm
By Purpose
Travel to work
Travel to school of college
Shopping trips
Social and recreational trips
Escort trips
Other trips
By Time of day
AM Peak
PM Peak
Off Peak
Aggregation Level
Person level trips
*Household level trips
*Zone level trips
Characteristics of Trips
Trip generation is performed before distribution and mode split, Therefore, in trip generation we cant use travel times, costs
These depend on knowing both origin and destination of the trip)
The total number of trips generated by a zone is assumed to be only a
function of:
Zonal attributes (population, employment, etc.)
Attributes of persons and activities in the zones (income, auto ownership, etc.).
Explanatory Variables
Home (production) end variables: population (by age, gender, etc.) number of workers (by occupation) household size auto ownership income distance from CBD .
Non-home (attraction) end variables: employment (retail, office, industrial, etc.) floor space (retail, office, industrial, etc.) .
Explanatory Variables
Various operational approaches to trip generation modelling:
Growth Rate Models
1. Trip rate models: Trips classified
2. Cross-classification (category analysis) models: Trip-makers classified
Regression models
1. Zonal
2. Household-based
3. Person-based
Trip Generation Modeling Approaches
Growth Factor Models
Simplistic method
T = G * t
Future number of trips is a function of:
Change in population,
Change in income,
Change in car ownership,
etc.
Future # of trips
Growth Factor
Current #of trips
Growth Factor (example)
Consider a zone i with 500 households
250 households (HHs) own cars
250 HHs do not own cars
Now, assume all HHs in zone i have a car in the future. How many trips will be produced?
If we assume all HHs will have a car in future what is the growth factor?:
Gi = projected car ownership/current car ownership
= 1 / 0.5 = 2
What is the projected number of trips produced by zone i?
Recall t = 2125 trips/day
Ti = Gi * ti
= 2 * 2125 = 4250 trips/day
Rates are typically associated with important generators within the region (land use)
Examples: Retail, services, manufacturing
Rates often in person-trips per thousand sq ft of land use
Rather than vehicle trips
NOTE: Planners must be careful to apply trip rate models in same context in which they were calibrated
Trip Rate Models
Trip Rate Model Example
Estimate the number of trips that will be generated by a new development with the following land-use characteristics:
Trip Rate Model Example
Make sure units
match up!
New trips
generated
Cross-classification
Also known as category analysis...similar to trip rate model
Classify households (or persons) by one or more variables
(e.g., household size AND # of cars).
Specific combinations of variables define household groups.
Assume that trip rates are relatively constant within each group.
Compute average trip rates for each group.
Zonal trips = sum of trips generated by all groups found in the zone
Provides highly detailed results
Potential Issues:
Requires large data sets
Lacks statistical goodness of fit measures
Does not require linearity (improvement)
Simple Cross-Classification Example
Household Location Vehicles Available
per Household
Persons per Household
1 2,3 4 5
Urban 0 0.57 2.07 4.57 6.95
1 1.45 3.02 5.52 7.9
2+ 1.82 3.39 5.89 8.27
Suburban 0 0.97 2.54 5.04 7.42
1 1.92 3.49 5.99 8.37
2+ 2.29 3.86 6.36 8.74
Rural 0 0.54 1.94 4.44 6.82
1 1.32 2.89 5.39 7.77
2+ 1.69 3.26 5.76 8.14
GIVEN: Daily Trip Rates (Trips per day) for each Household type
Average daily number of trips made by a HH (in a given zone)
in an urban location, with a single tenant owning 1 vehicle
Household Location Vehicles
Available per
Household
Persons per Household
1 2,3 4 5
Urban 0 100 200 150 20
1 300 500 210 50
2+ 150 100 60 0
Estimate the total number of trips that will be generated by the future population described:
GIVEN: Number of each household type for future population
Simple Cross-Classification Example
Expected number of HH (in the zone) in an urban
location, with a single tenant owning 1 vehicle
Household Location Vehicles Available per
Household Persons per Household
1 2,3 4 5
Urban 0 57 414 685.5 139
1 435 1510 1159.2 395
2+ 273 339 353.4 0
Simple Cross-Classification Example
COMPUTE: Future Trips Generated
To estimate total trips, sum the total trips for each household type:
Total Trips = 57+414+685.5+139+435+1510+1159.2+395+273+339+353.4+0
Total Trips generate by the zone = 5760.1 trips
= 1.45 trips/day * 300 HHs
Regression Development of an equation to predict the number of trips (per person, HH, zone) based on:
Population
Households
Car ownership
Accessibility
Number of dwellings
Employment
Etc.
The equation should relate our observed inputs and output
Objective is to estimate best fit linear relationships between dependent variable (#of trips) and one or more explanatory variables
The equation is calibrated to minimize errors
Model can be developed at the zonal or more disaggregate levels
Example: Two-variable model at the HH level:
Example: Two-variable model at zonal level:
Example: Multi-variable model at the zonal level:
Examples of Regression Models
Daily trip productions per household, all purposes 1.229 1.379 (#of vehicles per household)
Daily work trip attractions for a given zone 61.4 0.93 (Total zonal employment)
Work trip productions per zone
0.135 (Zonal population)
0.145 (Number of dwelling units per zone) -
0.253 (Total number of automobiles owned in the zone)
Regression Example (1 variable)
Y: Number of Daily Trips X: Household Size
8 3
13 7
6 3
7 2
7 3
6 2
7 3
8 4
5 2
11 5
9 4
5 2
9 5
11 6
6 2
9 4
o
o o
o o
o
o o
o o
o
x
Y
a
b
xi
Yi Observed data
{xi,yi}, i=1,,n
What values of a & b best
fit the observed data?
Y = a + bx
E, error, residual
Parameter Value Estimation
# d
ail
y t
rip
s
HH size
Regression Example (1 variable)
Y: Number of Daily Trips X: Household Size
8 3
13 7
6 3
7 2
7 3
6 2
7 3
8 4
5 2
11 5
9 4
5 2
9 5
11 6
6 2
9 4
Calibrated least square error line:
Y = 2.93 + 1.41 X
Number of Daily Trips
(dependent variable)
Household Size
(independent variable)
o
o o
o o
o
o o
o o
o
x
Y
a=2.93
b=1.41
xi
Yi
Y = a + bx
What is the model estimation at xi?
Yi =a + bxi ei
Parameter Value Estimation
Parameter Value Estimation
Parameter estimation for all models (linear or otherwise) involves:
Theoretically specifying the model functional form and its explanatory variables
Observing a representative sample of the systems behaviour
Defining the criterion defining best fit of the hypothesized model to the observed data
Developing a statistical valid, computationally efficient procedure for finding the best fit parameters for this problem
Evaluating the statistical performance of the estimated model and its goodness-of-fit
Regression Example (2 variables)
Number of Daily Trips Household Size Number of Vehicles 8 3 2
13 7 3
6 3 1
7 2 0 7 3 3 6 2 2
7 3 2 8 4 3
5 2 1 11 5 3 9 4 3
5 2 1 9 5 3
11 6 3 6 2 2
9 4 3
Calibrated least square error line: Y = 2.91 + 1.39 X1 + .03 X2
Additional Forms Linear:
T = 4.33 + 3.89 L1 0.005 L2 0.128 L3 0.012 L4 where
L1 = Vehicle ownership
L2 = Population density
L3 = Distance from CBD
L4 = Family income
(Source : Mertz and Hammer (1957) of BPR)
Exponential:
To = K1 Lo e -
1 t
o
Td = K2 Ld e -
2 t
d
(Source: Gupta and Hutchinson (1979))
Additional Forms
Multiplicative:
T = Po Pd Yo Yd Mo Md No t c fb .
P Population
Y Median Income
M Institutional character
N Transport supply
t Travel time
C Transport cost
F Departure frequency
(Source : Boston Washington corridor project)
Regression models are easy to construct and use.
BUT underlying assumptions, however, may be wrong:
1. Linearity.
2. No interaction between explanatory variables
3. best fit equations may give counterintuitive results
Things to keep in mind
Questions?
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