Identifying and Recognizing Usage Pattern of Electric Vehicles Using GPS and On-Board Diagnostics Data Xiaohong Chen, Ph.D. 1 ; Kunyun Li 2 ; Hua Zhang, Ph.D. 3 ; Quan Yuan, Ph.D. 4 ; and Qian Ye 5 1 Professor, Key Laboratory of Road and Traffic Engineering of the Ministry of Education, College of Transportation Engineering, Tongji Univ., Shanghai, China. Email: tongjicxh@163.com 2 Writer and Master, Key Laboratory of Road and Traffic Engineering of the Ministry of Education, College of Transportation Engineering, Tongji Univ., Shanghai, China. Email: tjkyl2014@163.com 3 Assistant Professor, National Maglev Transportation Engineering R&D Center, Tongji Univ., Shanghai, China (corresponding author). Email: xiaohai_hua@tongji.edu.cn 4 Key Laboratory of Road and Traffic Engineering of the Ministry of Education, College of Transportation Engineering, Tongji Univ., Shanghai, China. Email: quanyuan2019@outlook.com 5 Ph.D. Candidate, Key Laboratory of Road and Traffic Engineering of the Ministry of Education, College of Transportation Engineering, Tongji Univ., Shanghai, China. Email: yeqian122@163.com ABSTRACT The electric vehicle (EV) market in China has expanded substantially thanks to a series of incentive policies. The operational characteristics and usage patterns of EVs are different from those of traditional fuel vehicles (TFVs), especially in terms of energy savings and cruising duration of battery. With required energy management devices, EVs inadvertently generate fine- grained and continuous trajectory data, which provides an unprecedented opportunity to study the multi-person and multi-day usage patterns of EVs. The data set used in this study includes GPS and on-board diagnostics (OBD) information of 8,000 EVs including both plug-in hybrid electric vehicle (PHEVs) and battery electric vehicles (BEVs) within one-week in Shanghai. A multi-day activity data set is created for each user. The concept of entropy is introduced and modified to describe and distinguish the usage stability of each vehicle through the multi-day activity data set. Based on indicators including usage duration, travel mileage, and spatial parking location, the usage patterns of EVs could be classified into three modes: individual, commercial, and semi-commercial. Among all activities, over 4.5% is used fully for commercial, and 20% is used partly for e-hailing services. Furthermore, results reveal that PHEVs’ average travel distance of individual groups is significantly higher than that of BEVs. The findings have a profound long-term implication on urban traffic demand with the growth of EVs. The research expands the understanding of the changes brought by transport technological evolutions to human activity patterns, and provides quantitative support for the adjustment of electric vehicle supporting policies. Keywords: Electric vehicles; Trajectory data; Clustering; Usage pattern, 1. INTRODUCTION To relieve the growing traffic congestion, major Chinese megacities have extensively implemented policies to restrict the use of traditional fuel vehicles (TFVs). Examples include new license plate quotas, vehicle use restrictions based on the last digit of license plate numbers, International Conference on Transportation and Development 2020 85 © ASCE International Conference on Transportation and Development 2020 Downloaded from ascelibrary.org by Tongji University on 09/06/20. Copyright ASCE. For personal use only; all rights reserved.