A Peek inside the New Black Box:
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Transcript of A Peek inside the New Black Box:
A Peek inside the New Black Box:A Peek inside the New Black Box:
Understanding the Methodology of Understanding the Methodology of Knoxville’s New Accessibility-Based ModelKnoxville’s New Accessibility-Based Model
A Peek inside the New Black Box:A Peek inside the New Black Box:
Understanding the Methodology of Understanding the Methodology of Knoxville’s New Accessibility-Based ModelKnoxville’s New Accessibility-Based Model
Vince Bernardin, Jr., Ph.D.Vince Bernardin, Jr., Ph.D.Bernardin, Lochmueller & Associates, Inc.Bernardin, Lochmueller & Associates, Inc.
Vince Bernardin, Jr., Ph.D.Vince Bernardin, Jr., Ph.D.Bernardin, Lochmueller & Associates, Inc.Bernardin, Lochmueller & Associates, Inc.
Knoxville’s “Accessibility-based” Model Overview and notable features
Core methodology developed at Northwestern Motivation Understanding the problem The current solution A new solution The Traveler Conservation Constraint Trip-Chaining Effects in Stop Location Choice Experimental results
Agenda
Knoxville’s “Accessibility-based” ModelKnoxville’s “Accessibility-based” Model Overview and notable featuresOverview and notable features
Core methodology developed at Northwestern Motivation Understanding the problem The current solution A new solution The Traveler Conservation Constraint Trip-Chaining Effects in Stop Location Choice Experimental results
Agenda
March 12, 2009
Methodology1. A hybrid disaggregatedisaggregate / aggregateaggregate system
To maximize model fidelity and minimize run time
A New, Alternative Model Design
March 12, 2009
Network
TAZ
Flow Averaging
TrafficAssignment
HOV and TollChoices
Stop SequenceChoice
Stop LocationChoice
Tour ModeChoice
Activity / TourGeneration
PopulationSynthesizer
Accessibility
Travel Times
LinkFlows
Departure TimeChoice
Variables Models
Disa
ggregate M
odelsA
ggregate M
odels
Vehicle AvailabilityChoice
Example of Aggregation Bias
Consider mode choice 100 households with an average of 2.2 cars per
household 5 households with no cars5 households with no cars, 15 hh with one car,
50 hh with two cars, 20 hh with three cars, 5 hh with four cars, 5 hh with five
The full-information disaggregate picture looks a lot different than the aggregate picture of the same the same scenarioscenario – because aggregation entails information loss.
March 12, 2009
Methodology1. A hybrid disaggregatedisaggregate / aggregateaggregate system
To maximize model fidelity and minimize run time
2.2. Disaggregate vehicle & tour mode choicesDisaggregate vehicle & tour mode choices
3.3. Departure time choiceDeparture time choice
A New, Alternative Model Design
March 12, 2009
Network
TAZ
Flow Averaging
TrafficAssignment
HOV and TollChoices
Stop SequenceChoice
Stop LocationChoice
Tour ModeChoice
Activity / TourGeneration
PopulationSynthesizer
Accessibility
Travel Times
LinkFlows
Departure TimeChoice
Variables Models
Disa
ggregate M
odelsA
ggregate M
odels
Vehicle AvailabilityChoice
Vehicle Availability ORL Choice Model Each individual household chooses how
many vehicles to own / lease No aggregation bias Vehicle ownership levels respond to
Demographics (household size, income, number of workers, students, etc.)
Gas PricesGas Prices Transit AvailabilityTransit Availability Urban Design Urban Design (pedestrian environment
/ grid vs. cul-de-sac design)
No Veh
1 Veh
Nest
Nest
Root
2 Veh
Nest
3 Veh
4+ Veh
Choice Hierarchy
In traditional four-step models, mode choice was modeled conditional on (after) destination choice (due to a preoccupation with choice riders and commuting).
Instead, we modeled stop location or destination choice conditional on (after) mode choice
March 12, 2009
Network
TAZ
Flow Averaging
TrafficAssignment
HOV and TollChoices
Stop SequenceChoice
Stop LocationChoice
Tour ModeChoice
Activity / TourGeneration
PopulationSynthesizer
Accessibility
Travel Times
LinkFlows
Departure TimeChoice
Variables Models
Disa
ggregate M
odelsA
ggregate M
odels
Vehicle AvailabilityChoice
Choice Hierarchy
In traditional four-step models, mode choice was modeled conditional on (after) destination choice (due to a preoccupation with choice riders and commuting).
Instead, we modeled stop location or destination choice conditional on (after) mode choice We sequentially estimated combined (nested logit) combined (nested logit)
mode and stop location (and sequence) choice modelsmode and stop location (and sequence) choice models And all the logsum / nesting parameters were in the
acceptable ranges without using constraintswithout using constraints, suggesting that this may be the correct choice hierarchy
Choice Hierarchy
This reverse choice hierarchy reflects the fact that many travelers are more likely to change many travelers are more likely to change destinations than switch modesdestinations than switch modes Even for work tours, the data suggests that in
Knoxville, people are more likely to change jobs than change their travel mode to work
This may not be as unreasonable as it seems, considering captive riderscaptive riders, dependent on the bus to get to work
Imposing the traditional hierarchy traditional hierarchy may be a source of “optimism bias” “optimism bias” in transit forecasts
Departure Time Choice
Demand by 15-minute intervals based on: Travel time during period (peak-spreadingpeak-spreading) , and Bias variables interacted with sinusoidal functions:
Origin / Destination Accessibilities (urban vs. rural) Return factor (ratio of employment to population at origin
vs. destination) SOV vs. HOV trip
March 12, 2009
Methodology1. A hybrid disaggregatedisaggregate / aggregateaggregate system
To maximize model fidelity and minimize run time
2.2. Disaggregate vehicle & tour mode choiceDisaggregate vehicle & tour mode choice
3.3. Departure time choiceDeparture time choice
4. Feedback of ACCESSIBILITYACCESSIBILITY as well as travel time 1. To introduce sensitivity to ‘lower level’ choices in
‘upper level’ decisions
A New, Alternative Model Design
March 12, 2009
Network
TAZ
Flow Averaging
TrafficAssignment
HOV and TollChoices
Stop SequenceChoice
Stop LocationChoice
Tour ModeChoice
Activity / TourGeneration
PopulationSynthesizer
Accessibility
Travel Times
LinkFlows
Departure TimeChoice
Variables Models
Disa
ggregate M
odelsA
ggregate M
odels
Vehicle AvailabilityChoice
Network
TAZ
Flow Averaging
TrafficAssignment
HOV and TollChoices
Stop SequenceChoice
Stop LocationChoice
Tour ModeChoice
Activity / TourGeneration
PopulationSynthesizer
Accessibility
Travel Times
LinkFlows
Departure TimeChoice
Variables Models
Disa
ggregate M
odelsA
ggregate M
odels
Vehicle AvailabilityChoice
Travel Cost Elasticity Found elasticities of out-of-home activities with
respect to accessibility of 0.13 - 0.16 Lower tour-making by residents of ruralrural (lower-
accessibility) areas, Decreased tour/stop-making in response to
congestioncongestion (decreased accessibility), Induced tour/stop-making in response to added added
network capacitynetwork capacity (increased accessibility), Induced tour/stop-making in response to new land new land
use developmentsuse developments in other nearby zones (increased accessibility)
Network
TAZ
Flow Averaging
TrafficAssignment
HOV and TollChoices
Stop SequenceChoice
Stop LocationChoice
Tour ModeChoice
Activity / TourGeneration
PopulationSynthesizer
Accessibility
Travel Times
LinkFlows
Departure TimeChoice
Variables Models
Disa
ggregate M
odelsA
ggregate M
odels
Vehicle AvailabilityChoice
Residence Effects on Trip Length
When people choose their residence location, they also choose how far they are willing to travel.
We allowed travelers’ willingness-to-travel, and hence, trip lengths to vary as a function of the accessibility of their residence location The willingness-to-travel of residents of the most urban most urban
(most accessible) areas was about 10% lower 10% lower than the regional average
The willingness-to-travel of residents of the most rural most rural (least accessible) areas was about 200% higher 200% higher or twice the regional average for most activity types
Cost Elasticity from Accessibility
Including accessibility in both activity generation and stop location choice reflects fewerfewer, but longerlonger ruralrural tours; more shorter urban more shorter urban tours
March 12, 2009
Methodology1. A hybrid disaggregatedisaggregate / aggregateaggregate system
To maximize model fidelity and minimize run time
2.2. Disaggregate tour mode choiceDisaggregate tour mode choice
3.3. Departure time choiceDeparture time choice
4. Feedback of ACCESSIBILITYACCESSIBILITY as well as travel time 1. To introduce sensitivity to ‘lower level’ choices in
‘upper level’ decisions
5. A ‘double destination choicedouble destination choice’ framework1. Produce trips consistent w/ tours & tour cost minimization
A New, Alternative Model Design
March 12, 2009
Network
TAZ
Flow Averaging
TrafficAssignment
HOV and TollChoices
Stop SequenceChoice
Stop LocationChoice
Tour ModeChoice
Activity / TourGeneration
PopulationSynthesizer
Accessibility
Travel Times
LinkFlows
Departure TimeChoice
Variables Models
Disa
ggregate M
odelsA
ggregate M
odels
Vehicle AvailabilityChoice
Knoxville’s “Accessibility-based” Model Overview and notable features
Core methodology developed at NorthwesternCore methodology developed at Northwestern MotivationMotivation Understanding the problem The current solution A new solution The Traveler Conservation Constraint Trip-Chaining Effects in Stop Location Choice Experimental results
Agenda
Motivation
Trip distribution or destination choice is the largest source of errorlargest source of error in traditional travel models (Zhao & Kockelman, 2002)
Gravity models typically explain only about 20%-30%20%-30% of the variation in destination choices
Why are the models so bad? Assumption all travelers behave the same Lack of data (prices, parking, etc.) Assumption that these unobserved Assumption that these unobserved
variables are distributed randomlyvariables are distributed randomly Assumption all destination choices are Assumption all destination choices are
independent (no trip-chaining)independent (no trip-chaining)
Motivation Trips do not form closed tours
– physically impossible!physically impossible!
Trips are distributed inconsistently with tour cost minimization – behaviorally implausiblebehaviorally implausible
Existing solutions are costly
Knoxville’s “Accessibility-based” Model Overview and notable features
Core methodology developed at Northwestern Motivation Understanding the problemUnderstanding the problem The current solution A new solution The Traveler Conservation Constraint Trip-Chaining Effects in Stop Location Choice Experimental results
Agenda
Understanding the Problem
Two-fold problem with four-step spatial distributions Open tours Insensitivity to tour costs
March 12, 2009
An example of a possible trip table from a gravity model with seven trips (H-a, H-c, a-H, a-c, b-b, b-c, c-c): There is no way that all seven of these trips can be arranged
into one or more tours. Real travelers could not produce the travel pattern
in this trip table, but a four-step model can! For instance, one traveler doesn’t return home!
Open Tours H a b c
H 0 1 0 1 2
a 1 0 0 1 2
b 0 0 1 1 2
c 0 0 0 1 1
1 1 1 4 7
ab
H
c
March 12, 2009
Stops Locations (trip ends) which Minimize Tour Costs: Will be closer to home (radial dimension) Will be closer to each other (angular dimension)
In the Four-Step Model: Home-based trips minimize radial costs, but minimize radial costs, but NOTNOT angular angular Non-home-based trips minimize angular costs, but minimize angular costs, but NOTNOT radial radial
Tour Cost Minimization
H
a
bd
ef
z
x
y
c
Non-home-based Trips The four-step approach represents the
distribution of non-home-based trips as the result of a singlesingle gravity (destination choice) model
But NHB trips involve two destinations, so at least twotwo destination or stop location choices are needed
Knoxville’s “Accessibility-based” Model Overview and notable features
Core methodology developed at Northwestern Motivation Understanding the problem The current solutionThe current solution A new solution The Traveler Conservation Constraint Trip-Chaining Effects in Stop Location Choice Experimental results
Agenda
The Traditional (Sequential) Solution
Proposed by Shiftan (1998), used in all tour / activity-based models in U.S.
a
H
bATT = Hb+ba-Ha c
ATT = Hc+ca-Ha
The New Problem
Building tours sequentially Requires computationally intensive simulation Takes as many steps as stops Results in long model run times!
Knoxville’s “Accessibility-based” Model Overview and notable features
Core methodology developed at Northwestern Motivation Understanding the problem The current solution A new solutionA new solution The Traveler Conservation Constraint Trip-Chaining Effects in Stop Location Choice Experimental results
Agenda
A New Simultaneous Solution
First choose stop locations (where to go)Then choose how to sequence them (where to
go from)a
H
b
c
Network
TAZ
Flow Averaging
TrafficAssignment
HOV and TollChoices
Stop SequenceChoice
Stop LocationChoice
Tour ModeChoice
Activity / TourGeneration
PopulationSynthesizer
Accessibility
Travel Times
LinkFlows
Departure TimeChoice
Variables Models
Disa
ggregate M
odelsA
ggregate M
odels
Vehicle AvailabilityChoice
A New Simultaneous Approach
Advantage:Advantage: only two steps regardless of how many stops = fast run times!
Challenge 1:Challenge 1: how to insure that sequences form closed tours?
Challenge 2:Challenge 2: how to include the cost of non-home-based trips in the choice of stop locations?
a
H
b
c
Knoxville’s “Accessibility-based” Model Overview and notable features
Core methodology developed at Northwestern Motivation Understanding the problem The current solution A new solution The Traveler Conservation ConstraintThe Traveler Conservation Constraint Trip-Chaining Effects in Stop Location Choice Experimental results
Agenda
Traveler Conservation Constraint Requiring that whoever goes in, comes
out results in consistency with tours
,hij hj hj hj hjki k
T D T O T h j H a b c
f 0 5 0 3 8
g 1 1 0 3 5
H 22 33 11 11 77
a 1 4 0 2 7
4 13 1 9 27
H a b c
H 0 1 1 0 22
a 2 0 0 1 33
b 0 1 0 0 11
c 0 1 0 0 11
22 33 11 11 77
Shadow Prices The aggregate application allows the
enforcement of the constraint by iterative shadow pricing, following Andrew Stryker’s method for doubly constraining destination choice
ln ln
| ln ln
hj hj c ij
hj hj c ij
s D c
j hi s D c
j
eP
e
11
hjn nhj hj n
hiji
Ts s
T
March 12, 2009
An example of a possible trip table with a Traveler Conservation Constraint for seven trips (H-a, H-b, a-H, a-H, a-c, b-a, c-a): These trips could be produced by either the tours
H-a-H & H-b-a-c-a-HH-a-H & H-b-a-c-a-H H-b-a-H & H-a-c-a-HH-b-a-H & H-a-c-a-H
It can be proved that any trip table with identical row and any trip table with identical row and column sums is consistent with some set of tours.column sums is consistent with some set of tours.
Closed Tours H a b c
H 0 1 1 0 22
a 2 0 0 1 33
b 0 1 0 0 11
c 0 1 0 0 11
22 33 11 11 7
a
b
H
c
March 12, 2009
An example of a possible trip table from “forced symmetry” with a pathological tour The traveler could not make the pathological tour b-c-b because
they never visit b or c
The double destination choice framework minimizes The double destination choice framework minimizes this.this.
The probability of a pathological tour is related to the difference between the probability that the traveler will visit a subset of stops and the probability that the traveler will visit that subset of stops from home
Pathological Tours
h a b c
h 0 2 0 0 22
a 2 0 0 0 22
b 0 0 0 1 11
c 0 0 1 0 11
22 22 11 11 6
a
b
H
c
Under-determination of ToursIn general, there are far more possible
tours than (independent) trip probabilities, so the probabilities of tours cannot be determined from this model alone without further assumptions.
The approach is fastfast because it produces trips consistent with tours without determining the tours, themselves.
2
1
2 1S
s
s
Z Z Z
A New Simultaneous Approach
Advantage:Advantage: only two steps regardless of how many stops / tours = fast run times!
Challenge 1:Challenge 1: how to insure that sequences form closed tours?
Challenge 2:Challenge 2: how to include the cost of non-home-based trips in the choice of stop locations?
a
H
b
c
Knoxville’s “Accessibility-based” Model Overview and notable features
Core methodology developed at Northwestern Motivation Understanding the problem The current solution A new solution The Traveler Conservation Constraint Trip-Chaining Effects in Stop Location ChoiceTrip-Chaining Effects in Stop Location Choice Experimental results
Agenda
Stop Location ChoiceGravity Model
Stewart (1941), Huff (1963), Wilson (1967) Based on a single impedance (time from
home/origin) and employment / attractions Still most widely used (TRB, 2007)Still most widely used (TRB, 2007)
MNL Destination choice models Academic research (Ben-Akiva, 1973;
Lerman, 1976; Koppelman, 1977, 1978)
Recent applications in practice(Chow et al., 2005; Jonnalagadda et al., 2001)
Mostly traveler heterogeneity
Limitations of Standard MethodsBoth gravity and more general MNL models are
independent of the spatial arrangement or accessibility of alternative destinations
B
A
B
h
Scenario 1 Scenario 2
C
A
h
C
Independence
In traditionaltraditional models, two equidistantequidistant, equal-sizeequal-size destinations are equally probableequally probable.
What about Accessibility?
What if one is more accessiblemore accessible to other possible destinations?
ComplementarityMaybe the more accessiblemore accessible one is more probable -
because you have to go a nearby destination anyway, and so it’s convenient.
Higher accessibility means the expected costexpected cost of a possible
subsequent trip is lower.
Accessibility in Destination Choice
In 1984, Kitamura used an accessibility variable to incorporate trip-chainingtrip-chaining effects in destination choice
A New Simultaneous Approach
Advantage:Advantage: only two steps regardless of how many stops = fast run times!
Challenge 1:Challenge 1: how to insure that sequences form closed tours?
Challenge 2:Challenge 2: how to include the cost of non-home-based trips in the choice of stop locations?
a
H
b
c
March 12, 2009
But, maybe the less accessibleless accessible is more probable because half the time you go the other direction,
you go to a nearby alternative instead.
Substitution
Accessibility in Destination Choice
In 1984, Kitamura used an accessibility variable to incorporate trip-chainingtrip-chaining effects in destination choice
The prior year, Fotheringham used an accessibility variable to incorporate differential spatial competition differential spatial competition in destination choice
More recently,Bhat & collaborators have found one or the other in different cases
Different FindingsFotheringham finds substitutionsubstitution effects
Prob(C) in Scenario 1 >> Prob(C) in Scenario 2
Kitamura finds complementaritycomplementarity effectsProb(C) in Scenario 1 << Prob(C) in Scenario 2
B
A
B
h
Scenario 1 Scenario 2C
A
h
C
SubstitutionSubstitution and ComplementarityComplementarity
In some situations, only one effect may be present or dominate Fotheringham studied mostly migration and
long-distance travel where trip-chaining trip-chaining effects are neglegible
In other cases, both may be present but Kitamura / Fotheringham’s Competing Destinations (CD) models only capture a net effect
ACDC Models (Bernardin, Koppelman & Boyce, 2009)
Agglomerating and Competing Destination Choice (ACDC) Models Use 2 types2 types of accessibility:
Accessibility to complementscomplements (other places you need to go, regardless)
Accessibility to substitutessubstitutes (other places you might go, instead) ln
| ln
j c hjS
ASC
AC j
CAC j
j
Sh Sc A jj j
AS A
j AA
c
h S c
j
eP
e
March 12, 2009
ACDC Models attempt to minimize both dimensions of tour costs: Stops will be closer to homecloser to home (radial dimension) Stops will be closer to each othercloser to each other (angular dimension)
Tour Costs
H
a
bd
ef
z
x
y
c
ln ln
| ln ln
Sj AS j AC jk k
k
Sj AS j AC j k k
k
S A d B e
j h S A d B e
j
eP
e
βC
βC
c
c
β
β
c jkh
jc j k
j
h
c
c
A New Simultaneous Approach
Advantage:Advantage: only two steps regardless of how many stops = fast run times!
Challenge 1:Challenge 1: how to insure that sequences form closed tours?
Challenge 2:Challenge 2: how to include the cost of non-home-based trips in the choice of stop locations?
a
H
b
c
Knoxville’s “Accessibility-based” Model Overview and notable features
Core methodology developed at Northwestern Motivation Understanding the problem The current solution A new solution The Traveler Conservation Constraint Trip-Chaining Effects in Stop Location Choice Experimental resultsExperimental results
Agenda
Estimation ACDC stop location choice model
parameters were estimated From year 2000 household survey data for
Knoxville, Tennessee Using a genetic algorithm Based on Maximum Likelihood
With feasibility constraintsc<0, '<0, 'C<0, 'S<0 and AC>0
Estimation Results Multiple optima were observed
At least a local optimum was always found within the feasible region
HBM
-7419
-7418
-7417
-7416
-7415
-7414
-7413
-7412
-7411
-3 -2.5 -2 -1.5 -1 -0.5 0
alphas
log
like
liho
od
Home-Based Maintenance ACDC CD-PA CD Gravity (MNL)
Variable Parameter t stat
Parameter t stat
Parameter t stat
Parameter t stat
Attraction / Size / Quantity Variables
Retail Employment 3.1240 29.7 3.0896 29.5 3.0147 28.8 3.1569 29.6
Service Employment 1 1 1 1
THETA 1 1 1 1
Quality Variables
Average Travel Time (AVGTT) -0.25820.2582 -48.3 -0.25370.2537 -49.5 -0.2516-0.2516 -48.6 -0.26070.2607 -47.2
Accessibility to All Attractions -0.21500.2150 -13.2 -1.3044-1.3044 -10.9
- AVGTT in Accessibility -1.4389 *
Accessibility to Complements 0.82550.8255 6.6
- AVGTT in Acc. to Complements -0.4951 *
Accessibility to Similar -0.86050.8605 -9.3
- AVGTT in Acc. to Substitutes -0.9622 *
Log Likelihood at Convergence: -6666.20 -6683.62 -6703.57 -6761.00
Log Likelihood at Zeros: -9437.78 -9437.78 -9437.78 -9437.78
Rho Squared w.r.t. Zeros: 0.294 0.292 0.290 0.284
* Note: t-statistics were not calculated for embedded parameters since the significance of these effects should be evaluated by the parameter on the accessibility main effects.
ACDC Home-based Maintenance
Home-Based Other ACDC CD-PA CD Gravity (MNL)
Variable Parameter t stat
Parameter t stat
Parameter t stat
Parameter t stat
Attraction / Size / Quantity Variables
Population 1.6327 6.8 1.2983 5.9 1.3905 6.3 1.4524 7.3
Enrollment 1.2443 4.0 1.2134 4.1 1.2468 4.0 1.3061 4.5
Retail Employment 2.9590 14.1 3.1100 15.2 3.1265 14.8 3.1582 15.3
Service Employment 1 1 1 1
THETA 1 1 1 1
Quality Variables
Avgerage Travel Time (AVGTT) -0.2198-0.2198 -40.9 -0.21950.2195 -40.8 -0.2191-0.2191 -40.6 -0.2192-0.2192 -40.5
Accessibility to All Attractions -0.0251-0.0251 -1.5 -0.09500.0950 -0.6
- AVGTT in Accessibility -2.2885 *
Accessibility to Complements 8.19768.1976 3.9
- AVGTT in Acc. to Complements -1.3988 *
Accessibility to Similar -8.3318-8.3318 -3.9
- AVGTT in Acc. to Substitutes -1.3822 *
Log Likelihood at Convergence: -5428.78 -5438.22 -5439.16 -5439.35
Log Likelihood at Zeros: -7091.66 -7091.66 -7091.66 -7091.66
Rho Squared w.r.t. Zeros: 0.234 0.233 0.233 0.233
* Note: t-statistics were not calculated for embedded parameters since the significance of these effects should be evaluated by the parameter on the accessibility main effects.
ACDC Home-based Other
Forecasting Resulting probabilities from ACDC models
estimated with 2000 data were compared against new 2008 data.
ACDC models outperformed gravity models in forecasting even more than in the base year ACDC model offered 3.5%3.5% improvement improvement over the
explanatory power of gravity for HBM in 2000 ACDC model offered 6.7%6.7% improvement improvement
over the explanatory power of gravity for HBM in 2008
March 12, 2009
Policy Analysis & Planning
What happens if a newnew development occurs?
March 12, 2009
In current models, all the other destinations get equally less probable.
Policy Analysis & Planning
March 12, 2009
Complements get moremore probable – new trips to old destinations!
In ACDC models, nearby destinations are affected more than distant ones.
Policy Analysis & Planning
March 12, 2009
Sensitivity Analyses – Real World Examples
Comparison of gravity and ACDC models for three new developments to illustrate spatial competition and trip-chaining effects. A new factorynew factory employing 1,000 workers in Loudon county
indirectly attracts 125 daily non-work stops to the county.
March 12, 2009
Sensitivity Analyses Loudon County factory’s effect on shopping stops
March 12, 2009
Comparison of gravity and ACDC models for three new developments to illustrate spatial competition and trip-chaining effects. A new factorynew factory employing 1,000 workers in Loudon county
indirectly attracts 125 daily non-work stops to the county. A new Food Citynew Food City with 105 employees indirectly attracts a
NET 27 (+55-28) daily trips to nearby zones (halo effect)
Sensitivity Analyses – Real World Examples
March 12, 2009
Sensitivity Analyses New Food City’s effect on shopping stops
There’s no oneone “right” way of modeling travel behavior for every region.
There are a variety of different advanced model designs each with different pros and cons. This approach has some advantages, in terms of computational computational
efficiency efficiency / run time, And it allows incremental improvements incremental improvements from existing models But it probably works best in a semi-aggregatesemi-aggregate model
The ‘double destination choice’ framework allows for a new set of options for model designs.
Some Closing Thoughts
Thank You!