Transportation is one of the industrial sectors most impacted by global climate change. Electric vehicles are energy-efficient and often presented as a zero-emission transport mode to achieve longer-term de-carbonization visions in the transport sector. Governments are recognizing the highest priority of development of public transit policies for sustainability. Taxis are visible and thus electric vehicle use in taxi service can bring attention in urban life to a commitment towards sustainability in the public's opin-ion. ...
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Transportation is one of the industrial sectors most impacted by global climate change. Electric vehicles are energy-efficient and often presented as a zero-emission transport mode to achieve longer-term de-carbonization visions in the transport sector. Governments are recognizing the highest priority of development of public transit policies for sustainability. Taxis are visible and thus electric vehicle use in taxi service can bring attention in urban life to a commitment towards sustainability in the public's opin-ion. For this reason, this lecture note proposed a multi-agent system (MAS) approach incorporating electric vehicle dial-a-ride (DAR) operation and the appropriate car-pool and car-sharing schemes design for taxi service. The dial-a-ride operation problem consists of designing vehicle routes and schedules for users who specify pick-up and drop-off requests between origins and destinations. We have made some MAS simulation studies, which aims to minimize the total vehicle-distance travelled subject to meeting all advanced customers' requests, and constraints on vehicle capacity, pickup/ delivery time-window, customer ride-time and battery-charging restrictions. In this study, we designed vehicle dial-a-ride operation system and algorithm development for dynamic variants of elec-tric vehicles DAR, to enable on-line simulations of realistic scale for on-demand transit. We will also investigate robust solution approaches for the stochastic electric vehicles DAR. The insights obtained in studying these electric vehicles DAR variants would help to build an integrated planning model for location of charging stations and on-demand transit request management. This lecture note is expected to be read by academics (i.e. teachers, researchers and students), technology solutions developers and enterprise managers. The authors are expecting that the lecture note will contribute to the MAS technological concept in other applications. Finally, the authors are grateful to the readers for any constructive criticism.
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