1 transportation modeling approach direct vs. sequence meeghat habibian modeling approach
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Transportation Modeling ApproachDirect vs. Sequence
Meeghat Habibian
Modeling
approach
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(1) the direct approach.
MODELING APPROACHES
(2) the sequenced choice model approach. sequencing a series of models of choice and then combining them
a direct application of the concepts of microeconomic demand modeling
Approaches in travel demand modelingApproaches in travel demand modeling
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(1) the direct approach.
MODELING APPROACHES
(2) the structured choice model approach.
predicting the number of trips made in an urban area as a function of demand and supply characteristics
Approaches in travel demand modelingApproaches in travel demand modeling
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The Direct Approach:The Direct Approach:
The following attributes need to be identified:
1 purpose
2 origin
3 destination
4 mode
5 route
6 time of day
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X pijmrt
the number of trips made by an individual during a given
period of time, p=purpose, origin=i, destination=j, mode=m,
route =r, and at time of day= t
demand function:
all the attributes of all the alternatives simultaneously
The Direct Approach:The Direct Approach:
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Dp = vector of demand variables for trip purpose p
S ijmrt = vector of supply variables for trips with attributes
given by i, j, m, r and t
The Direct Approach:The Direct Approach:
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the total number of variables in the demand function:
d + ijmrt
In the quite realistic situation when d = 3, i= 3, j= 5,
M = 3, R = 2, and T =3,
the number would be 273
The Direct Approach:The Direct Approach:
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Simplifications in the Direct Approach models:Simplifications in the Direct Approach models:
Elimination of the cross-elasticities of demand for different
trip purposes, p, which has been assumed.
Eliminating the t index and constructing demand functions
for trips over all time periods (i.e., typical weekday).
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Simplifications in The Direct Approach models:
Another level of simplification is when origins and
destinations are left in the model (*aggregation on route and
modes), resulting in the origin-destination demand model or
a generation-distribution model:
The extreme of such a simplification is when all attributes
are suppressed except the trip origin or
a trip-generation model:
Simplifications in the Direct Approach models:Simplifications in the Direct Approach models:
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Example of The Direct Approach:Example of The Direct Approach:
One of the earliest direct demand models for an urban freeway
bridge in the San Francisco Bay Area, The Kraft-Wohl model
(1967) :
Trip volume
purpose
time of day
income measure
Population measure And …
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The Sequenced Choice Approach:The Sequenced Choice Approach:
The Direct Approach:
All the attributes of all the alternatives simultaneously
The Sequenced Choice Approach:
The number of trips is first decided, and then the other
attributes .
Sequential process
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Sequenced Choice
Approach
UTPS
Reversemodeling
The Sequenced Choice Approach Methods:
Two methods which are different in modeling trip generation
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The first method in sequence approach (UTPS)The first method in sequence approach (UTPS)
This method is common in practice:
Urban Transportation Planning System (UTPS)
A trip-generation model is defined Xpi, then distributed
among the alternatives available for mode, destination and
route choices, using models of travel choice.
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UTPS process:
trip-generation model
Mode spilt Assignment
distributing among the available destinations
Urban Transportation Planning System (UTPS)Urban Transportation Planning System (UTPS)
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The total travel demand is not elastic with respect to the
attributes of the supply system and that trips are generated
on the basis of demand variables only.
Attempts to correct this are made by either incorporating
aggregate measures of supply in the trip-generation model
(e.g., accessibility index)
UTPS major drawback
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proportion of all trips, that would select
route r route choice function
vector of supply
variables
set off all roads available
for this i,j,m
The second method in sequence approach (Reverse modeling)The second method in sequence approach (Reverse modeling)
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Using previous, provide a:
weighted average of the supply characteristics
modeling the conditional choice of mode:
Mode choice function
Reverse Modeling
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The weighted average of the supply characteristics to any
destination can be obtained:
The destination choice model can now be based on these
weighted supply values:
Destination choice function
Reverse Modeling
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the weighted average of all supply value from i:
a trip-generation demand model can be specified:
Reverse Modeling
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A transportation system serving an area:
1Purposea given trip purpose
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3
4
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originOne origin
Destination3 possible destinations
Modetwo modal networks
Routetwo routes
time of day--------------
Reverse Modeling example
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The travel times on
the network
The travel costs on
the network vector of
destinations
attractiveness
Reverse Modeling example
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Amounts of traffic flows from an origin i to destinations j by
each of the modes and routes?
The hierarchy assumed is, destination choice is first, and using that,
the choice of mode is made on the basis of which route is chosen .
1-modeling the choice of route conditional on mode choice:
Reverse Modeling example
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bases route choice only on travel times
Invariant respect to route
Reverse Modeling example
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Reverse Modeling example
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1- choice of route conditional on mode choice:
2-calculation of weighted average travel time for each mode
and destination combination:
Reverse Modeling example
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2-
for example: t11=(25)(0.39)+(16)(0.61)=19.51≈20 t12=(36)(0.4)+(24)(0.6)=28.8≈30
Reverse Modeling example
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3- A logit mode choice model:
Where V(m, j) is a linear choice of travel time & cost:
Reverse Modeling example
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3- computation of The weighted average values of the time
and cost functions Vˆ(j) for each destination:
Vˆ(j)=Σm V(m,j) p(m│j)
5.19=(5)(0,62)+(5.5)(0.38)
Reverse Modeling example
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4- A gravity destination choice model:
5- calculating p(m,r,j) matrix:
Stage 4
Stage 3
Stage 1
Reverse Modeling example
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5-
Reverse Modeling example
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6-Trip generation
measure of generalized transport cost
Xi =681
Reverse Modeling example
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7-allocating 681 trips among all the modes, routes, and
destinations according to the p(j,m,r) matrix
Reverse Modeling example
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1- choice of route conditional on mode choice.
2-calculation of weighted average travel time for each mode. and
destination combination.
3- modeling mode choice (a logit) .
4- modeling destination choice (a gravity).
5- calculating p(m,r,j) matrix.
6-computing Trip generation.
7-allocating all trips among all the modes, routes, and destinations .
Example summary