Dual Rebalancing Strategies for Electric Vehicle Carsharing Operations
The research team aims to test a new queueing network-based dynamic rebalancing strategy in test cases provided by ReachNow in Brooklyn, NY. The strategy can operate in a non-EV as well as an EV setting, so computational experiments for both will be conducted.
In addition, the researchers will develop a MATSim agent model of the study area in NYC, calibrate it based on household travel survey data from NYMTC, Openstreetmaps, traffic data from NYCDOT, and transit schedules from GTFS. Example screenshots of the uncalibrated base model are shown in Fig. 3.
The model will be calibrated with the non-EV carsharing fleet data from ReachNow. The simulation tool will be modified to embed the new rebalancing algorithm that the researchers are developing. Based on the data, the research team can then evaluate non-EV operations with the proposed rebalancing strategy and compare social welfare measures. EV operating data will not be available at the time of this project to evaluate an EV fleet with MATSim.
The team plans to end the project with a workshop for invited guests held jointly with Don Mackenzie’s research group from University of Washington so the two teams can share findings from their car sharing projects.
As a federally funded project, new data developed from this project will be made publicly accessible through C2SMART servers and on data repositories as noted in the C2SMART Data Management Plan. All original data from ReachNow (and derivative data) remains proprietary with ReachNow, and shared temporarily with C2SMART for the duration of the project.
The public deliverables for this project are:
- Generic rebalancing code that works with non-EV and EV systems
- Publication of tests comparing proposed rebalancing against existing operation
- Public data used for calibrating MATSIM model before adding ReachNow
- Updated carsharing extension for MATSIM that can handle proposed rebalancing algorithm
- Calibrated MATSIM model with uncalibrated ReachNow non-EV fleet
- Publication of decision support using MATSIM
|Principal Investigator||Joseph Chow, New York University|
|Funding Source||C2SMART Center: $135,501|
BMW ReachNow (cost-matching): $100,000 (in-kind)
NYU (cost-matching): $55,294
NYU AD (cost-matching): $52,641
|Total Project Cost||$343,436|
|USDOT Award #||69A3551747124|
|Start and End Dates||03/01/2018-05/31/2019|
|Implementation of Research Outcomes||The outcomes of this rebalancing strategy development can be immediately tested by ReachNow in a pilot deployment to compare performance against their current strategies for non-EV and EV fleets. It can also be used for other EV-based mobility-on-demand services in cities around the world. The evaluation of the rebalancing in an NYC model provides a tool for ReachNow and NYCDOT to plan charging infrastructure needs in NYC. When models are developed for other consortium cities (Seattle, El Paso, etc.), ReachNow and local agencies can evaluate the effect of deploying the same rebalancing strategy in other cities as well.|
|Impacts/Benefits of Implementation||By reducing operating costs for mobility on demand services, this research makes on-demand car sharing services more accessible for users and more sustainable for operators like ReachNow so that they can further expand to other unserved areas. Prior studies have shown adding one car sharing vehicle to a fleet can effectively remove 7 to 11 privately owned passenger cars from the road. This is especially important in an increasingly urban setting as the societal costs of vehicle-miles traveled increase with congestion.|