Data-Driven Distributionally Robust Electric Vehicle Balancing for Autonomous Mobility-on-Demand Systems Under Demand and Supply Uncertainties

Author:

He Sihong1ORCID,Zhang Zhili1,Han Shuo2ORCID,Pepin Lynn1,Wang Guang3ORCID,Zhang Desheng4,Stankovic John A.5ORCID,Miao Fei1ORCID

Affiliation:

1. Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA

2. Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, IL, USA

3. Department of Computer Science, Florida State University, Tallahassee, FL, USA

4. Department of Computer Science, Rutgers, The State University of New Jersey, Piscataway, NJ, USA

5. Department of Computer Science, University of Virginia, Charlottesville, VA, USA

Funder

NSF

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Reference60 articles.

1. Routing and Rebalancing Intermodal Autonomous Mobility-on-Demand Systems in Mixed Traffic

2. p2 charging: Proactive partial charging for electric taxi systems;yuan;Proc IEEE 39th Int Conf Distrib Comput Syst (ICDCS),2019

3. Real-Time Charging Station Recommendation System for Electric-Vehicle Taxis

4. Machine learning bias, statistical bias, and statistical variance of decision tree algorithms;dietterich,1995

5. Context-Aware Taxi Dispatching at City-Scale Using Deep Reinforcement Learning

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