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

1. Innovations in Freight Demand Modeling and DataA Transportation Research Board SHRP 2 Symposium A hybrid microsimulationmodelof urban freight travel demand Rick Donnelly | PB | 505-881-5357 | donnellyr@pbworld.com 15 September 2010

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

3. 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

4. High tech solution?

5. An agent-based approach

6. A hybrid approach

7. Model typology

8. Model overview Bootstrap Simulation

11. Data requirements

12. 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

13. Variance reduction (random sampling)

14. Sensitivity testing Important to get right • Average shipment weight • Value-density functions • Input-output matrix coefficients • Incidence of tours Relatively unimportant • Trip length averages or distributions • Truck type distribution • Operator shift limits • Number of stops/tour

15. Exercise results

16. 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

17. 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)