shrp2 c10a
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SHRP2 C10A. Final Conclusions & Insights. TRB Planning Applications Conference May 5, 2013 Columbus, OH Stephen Lawe, Joe Castiglione & John Gliebe Resource Systems Group. C10A Project Objectives. Current models are limited - PowerPoint PPT PresentationTRANSCRIPT
SHRP2 C10A
Final Conclusions & Insights
TRB PlanningApplications ConferenceMay 5, 2013Columbus, OH
Stephen Lawe, Joe Castiglione & John GliebeResource Systems Group
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C10A Project Objectives
Current models are limited Not sufficiently sensitive to the dynamic interplay
between travel behavior and network conditions Unable to represent the effects of policies such as
variable road pricing and travel demand management strategies
Advanced model systems can better represent demand changes and network performance
Peak spreading, mode choices, destination choices Capacity and operational improvements such as signal
coordination, freeway management and variable tolls, TDM
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C10A Model System
Model components exchange information in asystematic and mutually dependent manner
C10A model components Daysim “activity-based” model TRANSIMS network simulation model MOVES
C10A linked model system implemented in both Jacksonville, FL and Burlington, VT
“Linked” not “Integrated”
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How are the model system components linked?
Daysim activity-based model provides travel demand to TRANSIMS network simulation model
Minute-by-minute Parcel-to-parcel Detailed market segments (toll/notoll, trip-specific VOT) 1 hour to simulate 1 million people on laptop, ½ hour on server
TRANSIMS provides information on network performance by time-of-day, as detailed as:
10 minute skims Activity locations ~50 VOT classes in assignment
“Studio” controls model system execution and equilibration
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Application Considerations
Different policy questions require different methods for running the model system
Disaggregate framework Supports more detailed analysis Extracting, managing and
interpreting results is straightfoward
Volume of information is significant
Simulation variation Not an issue for activity-model Significant issue in network
simulation
Planning & Operations
Planning
Operations
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Conclusions
Integrated model system is more sensitive to a wider range of policies produces a wider range of statistics of interest to
decision-makers
Level of effort required to effectively test different types of improvements varied widely
Debugging the model system, and individual scenarios was the greatest challenge
Must have willingness to investigate and experiment
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Additional C10 Insights
Examples of sensitivity tests Linkage vs integration Equilibration and convergence Consistency
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Freeway Tolling: Demand Impacts
Trips shift out of peaks and midday and into evening and early AM
Higher tolls increases the magnitude of this shift
Time shifting varies by purpose
Work trips shift into early AM and out of AM peak
Social/recreation trips shift significantly out of peaks and primarily into the evening
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Work & Soc/Rec Trips by Time of DayBASE-WORK
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Difference in Trips by Time of Day
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Travel Demand Management
“Flexible Schedule” scenario Asserted assumptions about:
Fewer individual work activities Longer individual work durations Aggregate work durations
constant
Target: Fulltime Workers
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% o
f Tou
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Work Tour Duration Distribution
Original
Adjusted
Tours by Purpose (Fulltime Workers)Original Adjusted Adj/Orig
Work 94,408 78,472 0.83School 115 140 1.22Escort 8,070 9,023 1.12Pers Bus 13,519 16,848 1.25Shop 10,531 12,938 1.23Meal 3,817 3,842 1.01Soc/Rec 13,076 14,360 1.10Workbased 27,949 23,211 0.83Total 171,485 158,834 0.93
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Linkage vs Integration
Establishing linkages, not true integration C10 goal of working with the existing tools and
capabilities Integration may require more fundamental
reformulations “Demand” vs “Supply Models
Demand models as “planning models” – most build schedule a priori, and don’t reflect time-dependency throughout the day
DTA as “dynamic models” Mathematical formulations and behavioral theory
Lack of unifying behavioral theory Differences in formulation and foundations between
demand and supply models. Mathematical formulations should follow behavioral
theory
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Linkage Challenges
Equilibration & Uniqueness Unclear how to address within the context of
complex simulation tools Relevance to linked, advanced demand and
supply models Relevance to reality?
Need to consider multiple metrics Gap Consistency Stability
Practical issues of network supply runtime
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Convergence Testing
Convergence Necessary to ensure usefulness
of model system Given the same inputs, will the
model system produce the same outputs?
Can significantly influence the conclusions drawn
Network and system convergence
Extensive testing of different strategies
Network temporal resolution Successive iteration feedback Subselection
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MSim problems
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Lessons Learned: Application
Level of convergence can significantly influence the conclusions drawn from alternative analyses.
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Consistency
Convergence not meaningful if there are egregious inconsistencies
Temporal Spatial Typological
Example: demand model employs trip-segmented VOT, but then a single VOT used in network model
Activity models (typically) (Relatively) coarse temporal resolution Typological detail
Dynamic network models (typically) Temporal detail Coarse typological resolution
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Temporal Consistency
Even if consistent in structure or resolution, there can still be issues with outcome consistency
Ensure that the detailed schedules produced by the DaySim model are maintained in the TRANSIMS network model
Inconsistencies are inevitable – how to resolve
Maintain activity durations or departure times?
Allow supply model to reschedule
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Total Schedule Difference by Time-of-Day (Daysim Only)FIXED DEPARTURES: NO FURTHER ADJUSTMENTS
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Total Schedule Difference by Time-of-Day (Daysim Only)FIXED DEPARTURES: NO FURTHER ADJUSTMENTS
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Base
Spatial Detail
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Usual work location
Auto ownership
Person-day tour generation
Exact number of tours
Work tour time of day
Work tour mode
WB subtour generation
School tour mode
Other tour destination
Other HB tour time of day
Other HB tour mode
Intermediate stop generation
Intermediate stop location
Trip time of day
0% 20% 40% 60% 80% 100%
significant differenceinsignificant differencenot estimable
Estimated difference between Tampa and Jacksonville coefficient estimates% of coefficients by type of choice model
Transferability
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alt-specific constant
person characteristic
household characteristic
day-pattern characteristic
tour/trip characteristic
impedance measure
land use measure
time schedule measure
logsum from lower model
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significant differenceinsignificant differencenot estimable
Estimated difference between Tampa and Jacksonville coefficient estimates% of coefficients by type of variable
Transferability
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Future Efforts
Reconsideration of the fundamental “demand-supply” linkage
How can models be more tightly integrated? Can integrated solution methods be defined? Does equilibrium exist in reality, and if not what are the
implications?
How can advanced models be implemented and applied most effectively?