Behavioral Micro-Simulation

1. Behavioral Micro-Simulation Jose Holguin-Veras, Ph.D., P.E. William H. Hart Professor VREF’s Center of Excellence for Sustainable Urban Freight Systems Center for Infrastructure, Transportation, and the Environment Rensselaer Polytechnic Institute jhv@rpi.edu

2. Main goals • To produce a reasonable guess of freight traffic in metropolitan areas using: • Freight trip generation estimates (using NCFRP 25 models) • Known delivery patterns, such as tour length distributions by industry sectors (obtained from data collected by RPI from carriers and receivers) • Observed traffic counts at key corridors • The BMS was originally developed to assess the impacts of alternative policies to foster off-hour deliveries (7PM to 6AM)

3. Key components • Freight trip generation (FTG): estimated using the NCFRP 25 models and Zip Code Business Pattern data • Synthetic population of carriers (and receivers, if needed) is created • Using the data collected by RPI, the sample data is used to create the population of carriers needed to make all deliveries in the metro area • The origin of the deliveries are set to be the locations were warehouses and distribution centers are located • Delivery tours are created: • Match the tour length (number of stops) by industry sector • Match the number of deliveries by ZIP code (or any other level of geography used)

4. Graphically: Freight Trip Generation

5. Graphically: Synthetic population of carriers • Different industry sectors have different tour lengths • NYC and NJ (Holguin-Veras et al. 2012): • Average: 8.0 stops/tour; 12.6% do 1 stop/tour; 54.9% do < 6 stops/tour; 8.7% do > 20 stops • Synthetic population match observed traffic and FTG

6. Tour simulations • Select a truck in an industry sector • Number of stops is randomly assigned • Select receivers at random from the group of receivers in that sector • Compute optimal tour and store it • Repeat until delivery tours satisfy the FTG for the entire area 2) Five receivers 1) Origin of a truck that carries food products to five restaurants

7. Example: Use of the BMS in the OHD project

8. a) Base case (no OHD) b) Mixed operation BMS use in the off-hour delivery project Define range of incentives to receivers for OHD • Carrier/receiver synthetic generation • Randomly select industry segment • Generate/locate carrier • Generate/locate receivers to serve Legend: • Receiver behavioral simulation • Model receiver’s decision to accept OHD Carrier depot Regular-hour receiver Ordinal logit model (Holguin-Veras et al 2013) Off-hour receiver • Carrier behavioral simulation • Compute costs for base case and mixed operation • Model carrier’s decision Repeat for another carrier-receivers set Change incentives, reset participation counts End Output: Joint Market Share (JMS) of OHD Receivers Market Share (RMS) at TAZ level

9. Ordered logit model with random effects • This model reproduces receivers’ response to incentives Incentives NAICS code Interaction terms:OTI and NAICS Interaction terms:TV and NAICS

10. BMS Results OTI = $0avg = 2.2% max = 6.2% min = 0.6% • OTI = $4,000 • avg = 3.4% • max = 7.6% • min = 1.3% • OTI = $2,000 • avg = 2.7% • max = 7.6% • min = 1.2% • OTI = $6,000 • avg = 4.3% • max = 9.9% • min = 1.9% • OTI = $8,000 • avg = 5.5% • max = 11.9% • min = 2.6% • OTI = $10,00 • avg= 7.0% • max = 13.4% • min = 3.5%

11. Example: Geographically Oriented Incentives

12. Upper Manhattan (UM) Geographically focused incentives: case of NYC Central Park (CP) + • 50% of establishments are located in Midtown Manhattan being responsible for 52% of the incoming freight trips to the city • Two geographic distribution have been considered: (1) Lower and Midtown (2) Central Park and Upper • Scenarios consider giving incentivesto either the entire Manhattanor only to Lower and Midtown Manhattan Midtown Manhattan (MM) Lower Manhattan (LM) +

13. Results of geographically focused incentives • Ratio Budget/JMS provides an idea about the amount of resources required to achieve a 1% JMS • The results also show the superiority of geographically focused incentives which requires between 71% and 75% less expenditures than incentives spread out all over Manhattan

14. Example: Self-Supported Freight Demand Management

15. Self supported freight demand management • A self-supported freight demand management system (SS-FDM), is one that generates the funds required for a continuing improvement towards sustainability • The incentives to be handed out to the receivers are generated by a toll surcharge to the vehicles that travel in the regular hours • The analyses consider tolls to only trucks (per axle) or both; trucks and cars. Finally, different levels of toll collection efficiency were also considered

16. Results: tolls to trucks (per axle) Toll collection100% Toll collection 75% Note: The shaded cells represent non-feasible combinations of financial incentives to receivers and tolls.

17. Results: tolls to trucks (per axle) and cars Toll collection100% Toll collection 75% Note: in this case all combinations of financial incentives to receivers and tollsare feasible

18. Potential Uses

19. Potential Uses • The BMS will replicate freight traffic in any metro area • The BMS could be used to: • Produce realistic estimates of freight VMT • Analyze the impacts of alternative logistical configurations (using a Urban Consolidation Center, transfers of cargo to environmentally friendly modes like freight bicycles) • Analyze the impacts of retiming of deliveries, or receiver-led consolidation programs by receivers • Analyze the impacts of policies that change operational patterns, technologies, or infrastructure used bycarriers • Changes in work hours, limited emission zones, etc.

20. Expected outputs of the BMS • Acceptance rate of technology/ operations/ infrastructure in response to policy measures • Freight (large and small trucks) VMT by industry segment for the initiatives considered, including time of day for some • Freight traffic by origin-destination before/after, a key input for traffic simulation models • Cost impacts on carriers and receivers

21. Limitations • Estimation of air pollution • The BMS is not a traffic simulator, it does not account for traffic behavior in networks • Potential solution: • Use the BMS output as an input to traffic simulators • Purchase GPS data for key metro areas and post-process it with MOVES to produce estimates, add the estimates to BMS • The BMS is very good for urban freight modeling, though it does not consider intercity freight (and things like truck stop electrification, etc.) • Potential solution: create modules that perform these computations, add to BMS

22. Conclusions

23. Conclusions • The BMS is an important tool to evaluate TDM policies • The application to the Manhattan case study provides insight into the potential benefits, and limitations: • Off-Hour Deliveries • Geographic oriented incentives • Self Supported Freight Demand Management • Other extensions of the BMS include the analysis of incentives according to industry segments

24. Questions?