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Innovations in Freight Demand Modeling and Data A Transportation Research Board SHRP 2 Symposium . A hybrid microsimulation model of urban freight travel demand. Rick Donnelly | PB | 505-881-5357 | [email protected]. 15 September 2010. Policy context. Understanding. Economic linkages. - PowerPoint PPT Presentation
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Innovations in Freight Demand Modeling and DataA Transportation Research Board SHRP 2 Symposium
A hybrid microsimulation modelof urban freight travel demand
Rick Donnelly | PB | 505-881-5357 | [email protected] 15 September 2010
Policy context
Understanding
Forecasting
Economic competitiveness
Quantify externalities
Economic linkagesTruckrail diversion
Taxation
?
Crux of the problem
FirmsProduction functionsVolume of shipmentsGoods producedFrequency of shipments
NetworksLevels of congestionTruck volumesCrashes
TrucksOperating characteristicsTemporal patternsTraffic counts
High tech solution?
An agent-based approach
Agents Objects
Entities “The economy”ShippersCarriersIntermediariesConsumersRegulators
ShipmentsVehiclesFacilitiesTransport networksInformation networks
Attributes MobileGoal-orientedAdaptiveLoosely coupledStochastic behaviorLocal view
Variable mobilityContextualNot self-directedDeterministic behaviorGlobal optimisation possible
58,106725,400
1,620?
12305
49,109112,106
firmshouseholdstraffic analysis zonescarriersexportersimporterstrucksshipments
221,258 agents
A hybrid approach
Agents Objects
Entities “The economy”ShippersCarriersIntermediariesConsumersRegulators
ShipmentsVehiclesFacilitiesTransport networksInformation networks
Attributes MobileGoal-orientedAdaptiveLoosely coupledStochastic behaviorLocal view
Variable mobilityContextualNot self-directedDeterministic behaviorGlobal optimization possible
Model typology
Mathematical equations (deterministic outcomes) Estimation of gross urban product Translation of gross urban product to (value of) commodities Translation of value of commodities from annual value to weekly tons Tour optimization using traveling salesman problem (TSP) algorithm Traffic assignment (EMME/2 multi-class assignment by period)
Sampling from statistical distributions or generated by rules (stochastic outcomes) Decision whether to ship when total value falls below threshold Generation of discrete shipments from total tons shipped Discrete choice of destination firm and its distance from shipper Firm’s choice of carrier Incidence of trans-shipment (including distribution centers) Choice of import and export agents Carrier’s choice of vehicles Number of hauls (tours) per day Selection of routing inefficiency factors
Simulation
Bootstrap
Model overview
Simulation
Bootstrap
Simulation
Bootstrap
Data requirements
Source Data requirement(s)Commodity Flow Survey (CFS) Value-to-ton ratios
Mode shares by commodityLong distance trip lengths
Vehicle Inventory and Use Survey (VIUS)
Average weekly miles by commodityDistribution of carrier type by commodityDistribution of truck type of commodityAverage stops per week
Truck intercept surveys Average and total shipment weights by truck type
Employment by firm Attribution of Firm agentsDiscrete destination choice
Make and use coefficients Shipment generationDiscrete destination choice
Truck counts Attribution of Import and Export agentsModel assessment and validation
Exercising the model
Building a reference case Monte Carlo simulation vs. random sampling Variance reduction Sensitivity testing Validation
Compare to system optimal assignment Relocate trans-shipment centres Reduce private carriage
Variance reduction (random sampling)
Sensitivity testing
Important to get right1. Average shipment weight2. Value-density functions3. Input-output matrix
coefficients4. Incidence of tours
Relatively unimportant1. Trip length averages or
distributions2. Truck type distribution3. Operator shift limits4. Number of stops/tour
Exercise results
Process validation (after Barlaz, 1996)
Parameter confirmationExtreme condition testingModel alignment
Structure confirmation testExternal examination
Stress testingTuring tests
Pattern prediction testsOverall summary statistics
Conclusions
Successful proof of concept Robust emergent behaviour Validates city logistics schemes
Agents are cool, but… Don’t scale to large problems Cannot optimise emergent agent behaviour Calibration and validation uncharted territory
Hybrid approach is feasible Reactive agents (firms, carriers, etc.) Objects (vehicles, shipments, sensors) Environment (geographic backplane, networks)