Transcript Transit Estimation and Mode Split CE 451/551 Source: NHI course on Travel Demand Forecasting (152054A) Session 7

Transit Estimation and
Mode Split
CE 451/551
Source: NHI course on
Travel Demand
Forecasting
(152054A) Session 7
Terminology
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HOV
Light Rail Portland; Florence
Heavy rail
Commuter rail
Local bus service
Express bus service
Paratransit service
Busways
Headways/frequency
Transit captive
Factors Affecting Mode Split
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Person/household characteristics
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Trip characteristics
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Purpose, chaining, time of departure, OD, length
Land use characteristics
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Auto availability, income, HH size, life cycle
Sidewalk/ped facilities, mix of uses at both ends, distance to
transit, parking and costs at both ends, density at both ends
Service characteristics
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Facility design (HOV, bikes), frequency, congestion, cost (parking,
tolls, fares, out-of-pocket costs), stop spacing
Mode Split Model Applications
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Route or service changes
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effect of changes in cost, frequency, transfer
system, more or less service and routes
Not usually modeled with TDF (use analogy or
elasticity)
Major investment studies, e.g. HOV, New rail
or other capital investment project design
Policy changes
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Parking, urban growth boundaries, congestion
pricing
Mode Split Strategies
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Analogy
Elasticity Analysis
Direct Estimation of Transit Share
Disaggregate Mode Split
Choosing a Mode Split Technique
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Application
Time and budget constraints
Project costs
Existing data availability
Existing service?
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if none, have to “borrow” a model
Selecting Analogy Routes
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Selection based on similarities in:
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Household characteristics
Transit service
Adjustments
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Service area household characteristics
Service differences
Fare differences
Elasticities:
ratio of change in
demand over
change in system
Example of Elasticity
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If transit fares are raised from $1.00 to $1.25
and there is a resulting drop in daily transit
ridership from 8,000 to 7,200, the elasticity,
as calculated below, would be -0.40
Elasticity analysis example
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What does the –0.4 factor mean?
typical values for cities range from
-0.15 to -0.4
Is this elastic, or inelastic?
Do you think larger cities would have larger
or smaller elasticity? Why?
Direct Estimation of Transit Share
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In small-to-medium regions with limited transit use
Particularly when transit use is limited to specific
populations (zero-car household, students, and
elderly)
Generally estimate district-to-district transit share
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Find relationship between SE&D and %transit
Calibrate for base year
Assume relationship will hold in future
Subtract resulting transit trips from person trip table.
Disaggregate Mode Split Models
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Travel is a result of choices
Elasticity, analogy, and direct estimation of
transit share are limited, particularly in policy
analysis
Output
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Share of person trips using each mode (by trip
purpose) for each production-attraction cell.
Disaggregate Mode Split Models
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Utility functions
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Probability models (overcomes limitations of deterministic
utility functions)
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Logit the most common
Incorporate utility equations into probabilistic equations
Binomial logit models
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Building blocks for DMS models
Rank desirability of the alternate transportation modes
Deterministic equations
Predict choice between two alternatives
Multinomial logit models
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Predict choice between more than two alternatives
Disaggregate mode split using Utility
Functions and Probabilistic Models
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Input: Individual responses on mode desirability
and usage to develop “Utility functions”
Preference and usage data may be from census or
special home surveys.
System data such as travel time and cost generally
from network data
usually don’t have the kind of data needed to know
all users preferences
Observation v. prediction
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If we wish to estimate transit by income level
(or other detailed variable) in the future we
need to be able to forecast the population
characteristic in each group.
The more disaggregate the data set for
modeling, the more difficult the prediction of
future.
Just like trip generation and distribution … can you give examples?
Probability Equations
Binomial Logit Model Example
Auto Utility Equation: UA= -0.025(IVT) -0.050(OVT) - 0.0024(COST)
Transit Utility Equation: UB= -0.025(IVT) -0.050(OVT) – 0.10(WAIT) –
0.20(XFER) - 0.0024(COST)
Where:
IVT= in-vehicle time in minutes
OVT = out of vehicle time in minutes
COST = out of pocket cost in cents
WAIT = wait time (time spent at bus stop waiting for bus)
XFER = number of transfers
Question: what is the implied cost of IVT? OVT? WAIT? XFER?
References
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Transit Fact Book, 50th ed, American Public Transit
Association, Washington, D.C. January 1999.
Federal Highway Administration. Traveler Response to
Transportation System Changes. 2nd ed, U.S. Department of
Transportation, Washington, D.C., July 1981.
Federal Transit Administration, A Self-Instructinf Course in
Disaggregate Mode Choice Modeling. Report No. DOT-T-9319. U.S. Department of Transportation, Washington, D.C.,
December 1986
Meyer, M.D., and E.J. Miller. Urban Transportation Planning,
A Decision-Oriented Approach. 2nd ed. McGraw-Hill, 2001.
Homework
Network Data
In-vehicle Time
Out of Vehicle Time Cost
Calculate Mode Shares
Mode
OVT
IVT
Cost (cents)
1 person
5
17
200.0
2-person carpool
5
21
100.0
3-person carpool
5
23
66.6
4-person carpool
5
25
50.0
Transit
7
33
160.0
Part 1: CALCULATE MODE
PROBABILITIES BY MARKET SEGMENT
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Overview: Calculate the mode probabilities for the trip
interchanges. Use the tables on the next pages.
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Part A: Calculate the utilities for transit as follows:
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Insert in the table the appropriate values for OVT, IVT, and
COST.
Calculate the utility relative to each variable by multiplying the
variable by the coefficient which is shown in parenthesis at the
top of the column; and
Sum the utilities (including the mode-specific constant) and put
the total in the last column.
Part B: Calculate the mode probabilities as follows:
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Insert the utility for transit in the first column;
Calculate eU for transit
Sum of eU for transit and put in the “Total” column; and
Calculate the probability for transit using the formula:
Sum the probabilities (they should equal 1.0)
Say, from trip distribution, the number of trips was 14,891.
Calculate the number of trips by mode using the
probabilities calculated.
Mode
Trips
(Zone 5 to Zone 1)
Solo Driver
2-Person Carpool
3-Person Carpool
4-Person Carpool
Transit
Total
14, 891
If we had time …
Source:
publicpurpose.com
Cheaper to lease cars than
provide new transit?
http://www.publicpurpos
e.com/ut-2000rail.htm
Transit share dropping?
http://www.publicpurpos
e.com/ut-intlmkt95.htm
Where rail transit works
http://www.publicpurpos
e.com/utx-rails.htm
You can see an alternative view
here:
http://www.sprawlwatch.
org/