title: Modeling faults among e-bike-related fatal crashes in China.
abstract: This article aims to model fault in e-bike fatal crashes in a county-level city in China.
  
  
  
  Three-year crash data are retrieved from the crash reports (2012-2014) from the Taixing Police Department. A mixed logit model is introduced to explore significant factors associated with fault assignment, as well as accounting for similarity among fault assignment and heterogeneity within unobserved variables.
  
  
  
  The modeling results indicate some interesting new findings. First, precrash behaviors of both drivers and e-bike riders are found to be significant to fault assignment. Second, bike lane and median type are significantly associated with e-bike rider fault commitment. Third, specific groups of e-bike riders (low-educated and older) and drivers (heavy good vehicles) are more likely to be at fault in e-bike crashes. Last, crash location and the built environment have significant correlations with faulty behaviors of e-bike riders.
  
  
  
  Safety countermeasures are proposed including (1) the deployment of traffic design and control elements including physically separated bike lanes, medians, video surveillance systems for e-bike riders, and left-turning treatments for nonmotorists (e.g., a 2-step e-bike left turning); (2) the amendment of the current traffic regulations on drunk e-bike riders and child e-bike passengers; (3) the development of a license system for specific e-bike rider groups (older and low-educated) and a safety campaign for drivers (to increase safety awareness when parking on-street or driving heavy good vehicles). Some interesting future research topics are also suggested: e-bike riders' behaviors at unsignalized intersections and mid-block openings, e-bike safety in suburban areas, and an in-depth study of the effect of the built environment on e-bike safety.
authors.affiliation: a Jiangsu Key Laboratory of Urban ITS, Southeast University , Nanjing , China.|b Intelligent Transportation Research Center, Southeast University , Nanjing , China.
authors.forename: Chen
authors.initials: C
authors.lastname: Wang
authors.affiliation: a Jiangsu Key Laboratory of Urban ITS, Southeast University , Nanjing , China.
authors.forename: Chengcheng
authors.initials: C
authors.lastname: Xu
authors.affiliation: b Intelligent Transportation Research Center, Southeast University , Nanjing , China.
authors.forename: Jinxin
authors.initials: J
authors.lastname: Xia
authors.affiliation: b Intelligent Transportation Research Center, Southeast University , Nanjing , China.
authors.forename: Zhendong
authors.initials: Z
authors.lastname: Qian
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D000328:Adult; D000368:Aged; D001334:Automobile Driving / Q000523:psychology*; D002681:China; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D009049:Motorcycles / Q000706:statistics & numerical data*; D018570:Risk Assessment; D012309:Risk-Taking; D055815:Young Adult
keywords: e-bike fatal crashes; fault; mixed logit model; safety countermeasures