1. Behavioral Micro-Simulation 1 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) 2

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) 3

4. Graphically: Freight Trip Generation 4

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 20 stops  Synthetic population match observed traffic and FTG 5

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 6 1) Origin of a truck that carries food products to five restaurants 2) Five receivers

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

8. BMS use in the off-hour delivery project 8 Carrier/receiver synthetic generation  Randomly select industry segment o Generate/locate carrier o Generate/locate receivers to serve Receiver behavioral simulation  Model receiver’s decision to accept OHD Carrier behavioral simulation  Compute costs for base case and mixed operation  Model carrier’s decision Repeat for another carrier-receivers set End Change incentives, reset participation counts Define range of incentives to receivers for OHD Ordinal logit model (Holguin-Veras et al 2013) Regular-hour receiver Off-hour receiver a) Base case (no OHD) b) Mixed operation Carrier depot Legend: 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 9 Incentives Interaction terms: OTI and NAICS NAICS code Interaction terms: TV and NAICS

10. BMS Results 10 OTI = $0 avg = 2.2% max = 6.2% min = 0.6% OTI = $2,000 avg = 2.7% max = 7.6% min = 1.2% OTI = $4,000 avg = 3.4% max = 7.6% min = 1.3% 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. Geographically focused incentives: case of NYC  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 incentives to either the entire Manhattan or only to Lower and Midtown Manhattan Lower Manhattan (LM) Midtown Manhattan (MM) Central Park (CP) Upper Manhattan (UM) + +

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 13

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 15

16. Results: tolls to trucks (per axle) Toll collection 100% Toll collection 75% 16 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 collection 100% Toll collection 75% 17 Note: in this case all combinations of financial incentives to receivers and tolls are feasible

18. Potential Uses

19.  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 by carriers  Changes in work hours, limited emission zones, etc. 19

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 20

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 21

22. Conclusions

23.  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 23

24. Questions? 24