Experience: Understanding Long-Term Evolving Patterns of Shared Electric Vehicle Networks∗ Guang Wang Xiuyuan Chen Fan Zhang Rutgers University Rutgers University Shenzhen Institutes of Advanced
[email protected] [email protected] Technology, CAS
[email protected] Yang Wang Desheng Zhang University of Science and Rutgers University Technology of China
[email protected] [email protected] ABSTRACT CCS CONCEPTS Due to the ever-growing concerns on the air pollution and • Networks → Network measurement; Mobile networks; energy security, many cities have started to update their Cyber-physical networks. taxi fleets with electric ones. Although environmentally friendly, the rapid promotion of electric taxis raises prob- KEYWORDS lems to both taxi drivers and governments, e.g., prolonged Electric vehicle; mobility pattern; charging pattern; evolving waiting/charging time, unbalanced utilization of charging experience; shared autonomous vehicle infrastructures and reduced taxi supply due to the long charg- ing time. In this paper, we make the first effort to understand ACM Reference Format: the long-term evolving patterns through a five-year study Guang Wang, Xiuyuan Chen, Fan Zhang, Yang Wang, and Desh- on one of the largest electric taxi networks in the world, i.e., eng Zhang. 2019. Experience: Understanding Long-Term Evolving the Shenzhen electric taxi network in China. In particular, Patterns of Shared Electric Vehicle Networks. In The 25th Annual we perform a comprehensive measurement investigation International Conference on Mobile Computing and Networking (Mo- called ePat to explore the evolving mobility and charging biCom ’19), October 21–25, 2019, Los Cabos, Mexico. ACM, New York, patterns of electric vehicles.