Innovations in Freight Demand Modeling and Data A Transportation Research Board SHRP 2 Symposium A...

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 | donnellyr@pbworld.com 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 ratiosMode 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 right

1. Average shipment weight

2. Value-density functions

3. Input-output matrix coefficients

4. Incidence of tours

Relatively unimportant

1. Trip length averages or distributions

2. Truck type distribution

3. Operator shift limits

4. 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)