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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

With the great increase of electric vehicles (EVs) in the past decade, EV-involved traffic accidents have also been increasing quickly in many countries, bringing many new traffic safety challenges. Norway has the largest EV penetration rate in the world. Using the EV accident data from Norway in 2020 and 2021, this study aims to investigate the features of EV safety comprehensively. Firstly, a descriptive analysis is conducted. It has been found that rear-end collisions are the major collision type of EVs, and EVs are very likely to collide with pedestrians/cyclists. In addition, in terms of roadway type, EV accidents mainly occur on medium- and low-speed roads; in terms of environment, they mainly occur in good visibility conditions and dry road surface conditions. Then, a regression analysis is conducted to identify the key factors affecting the accident size, which is the number of traffic units involved in an accident and taken as the accident severity surrogate here. Since EV accidents are divided into four categories in order of accident size, the ordered logit model is adopted. It divides a multi-categorical dependent variable into multiple binary data points in order and calculates the probability of the dependent variable falling into each category with the logit model, respectively. The estimation results indicate that time of day, speed limit, and presence of medians have statistically significant impacts on the EV accident size. Finally, some countermeasures to prevent EV accidents are proposed based on the research results.

Details

Title
Examination of the Factors Influencing the Electric Vehicle Accident Size in Norway (2020–2021)
Author
Hou, Xuerui 1 ; Su, Meiling 1 ; Liu, Chenhui 2 ; Li, Ying 3 ; Ma, Qinglu 4 

 College of Civil Engineering, Hunan University, Changsha 410082, China; [email protected] (X.H.); [email protected] (M.S.) 
 College of Civil Engineering, Hunan University, Changsha 410082, China; [email protected] (X.H.); [email protected] (M.S.); Key Laboratory of Highway Engineering of Ministry of Education, Changsha University of Science & Technology, Changsha 410114, China; Research Institute of Hunan University in Chongqing, Chongqing 401120, China 
 School of Information Engineering, Chang’an University, Xi’an 710064, China; [email protected] 
 School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China; [email protected] 
First page
3
Publication year
2024
Publication date
2024
Publisher
MDPI AG
e-ISSN
20326653
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3081747486
Copyright
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.