1. 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 | donnellyr@pbworld.com 15 September 2010

2. Policy context Understanding Forecasting Economic competitiveness Quantify externalities Economic linkages Truck  rail diversion Taxation

3. The ideal model Explicit linkages to economic forecasts Places the study area in a global trading context Capture important dynamics Multiple decision-makers Trip chaining Trans-shipment Competition Multimodal Translate commodity flows to modal vehicle flows Robust truck-rail diversion analysis capability Policy sensitive Sparse data requirements

4. ? Crux of the problem Firms Production functions Volume of shipments Goods produced Frequency of shipments Networks Levels of congestion Truck volumes Crashes Trucks Operating characteristics Temporal patterns Traffic counts

5. High tech solution?

6. Model design decisions macroeconomic approach aggregate formulation disaggregate formulation micro- simulation agent-based model parametric model supply chain approach direct demand approach model requirements build model Pluses: Handles heterogeneity Accepts disparate data Flexible Emergent behavior Illuminates interactions Modest data required Minuses: Stochastic Computationally heavy Distributed computing? Unproven Validation unknown Unknown scalability

7. An agent-based approach Agents Objects Entities“The economy” Shippers Carriers Intermediaries Consumers Regulators Shipments Vehicles Facilities Transport networks Information networks AttributesMobile Goal-oriented Adaptive Loosely coupled Stochastic behavior Local view Variable mobility Contextual Not self-directed Deterministic behavior Global optimisation possible 58,106 725,400 1,620 ? 12 305 49,109 112,106 firms households traffic analysis zones carriers exporters importers trucks shipments 221,258agents

8. A hybrid approach AgentsObjects Entities“The economy” Shippers Carriers Intermediaries Consumers Regulators Shipments Vehicles Facilities Transport networks Information networks AttributesMobile Goal-oriented Adaptive Loosely coupled Stochastic behavior Local view Variable mobility Contextual Not self-directed Deterministic behavior Global optimization possible

9. 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

10. Simulation Bootstrap Model overview

12. Input-output matrices Use Make

14. Data requirements SourceData requirement(s) Commodity Flow Survey (CFS)Value-to-ton ratios Mode shares by commodity Long distance trip lengths Vehicle Inventory and Use Survey (VIUS) Average weekly miles by commodity Distribution of carrier type by commodity Distribution of truck type of commodity Average stops per week Truck intercept surveysAverage and total shipment weights by truck type Employment by firmAttribution of Firm agents Discrete destination choice Make and use coefficientsShipment generation Discrete destination choice Truck countsAttribution of Import and Export agents Model assessment and validation

15. Portland region Employment: 1,210,200 Wage & salary jobs 958,000 Self-employed 252,200 Population: 1,874,500 Households: 725,400

16. 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

17. Variance reduction (random sampling)

18. Monte Carlo simulation Sampled Value-tons Shipment size Destination firm type Destination firm distance Type of carrier Type of truck Departure time

19. 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

20. Exercise results

21. Process validation (after Barlaz, 1996) Parameter confirmation Extreme condition testing Model alignment Structure confirmation test External examination Stress testing Turing tests Pattern prediction tests Overall summary statistics

22. 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)