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 [email protected]
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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.
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Geographically focused incentives: case of NYC50% 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
Lower Manhattan (LM)
Midtown Manhattan (MM)
Central Park (CP)
Upper Manhattan (UM)
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Results of geographically focused incentivesRatio 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
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
Freight vehicle surcharge per axle:One-time-incentive
% OHDOHD tours (year)
Total incentive
budget
Yearly incentive
budget
Yearly toll revenues (car surcharge = $1)
Note: in this case all combinations of financial incentives to receivers and tolls are feasible
Potential Uses
Potential Uses
The BMS will replicate freight traffic in any metro area
The BMS could be used to:Produce realistic estimates of freight VMTAnalyze 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.
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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
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Limitations
Estimation of air pollutionThe BMS is not a traffic simulator, it does not account
for traffic behavior in networksPotential 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
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Conclusions
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 DeliveriesGeographic oriented incentivesSelf Supported Freight Demand Management
Other extensions of the BMS include the analysis of incentives according to industry segments