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This paper presents the design of an Eco-Driving Assistant System (EDAS) in which the main goal is to minimize the energy use of battery electric vehicles, in particular, vehicles utilized for public transportation. The system optimizes the speed profile of a real route schedule while satisfying the constraints imposed on speed and time. It includes a driver feedback and a driver scoring GUI which allows the driver improving his/her driving skills and comparing him/herself to a “theoretical perfect driver”. The system also includes a backward simulator that generates information related to the vehicle operation under the particular route to be optimized. The output information from the simulator is used as an input to the optimization algorithm. The simulator was validated using real data from a battery electric vehicle. The EDAS system was tested for three different driving profiles and energy consumption reductions of up to 30.33% were achieved.

The current business model of the automotive industry is based on individual car ownership, yet new ridesharing companies such as Uber and Lyft are well capitalized to invest in large, commercially operated, on-demand mobility service vehicle fleets. Car manufacturers like Tesla want to incorporate personal car owners into part-time fleet operation by utilizing the company’s fleet service. These robotaxi fleets can be operated profitably when the technology works in a reliable manner and regulators allow driverless operation. Although Mobility-as-a-Service (MaaS) models of private and commercial vehicle fleets can complement public transportation models, they may contribute to lower public transportation ridership and thus higher subsidies per ride. This can lead to inefficiencies in the utilization of existing public transportation infrastructure.