Identifying factors affecting the safety of mid-block bicycle lanes considering mixed 2-wheeled traffic flow.
Electric bikes (e-bikes) have been one of the fastest growing trip modes in Southeast Asia over the past 2 decades. The increasing popularity of e-bikes raised some safety concerns regarding urban transport systems. The primary objective of this study was to identify whether and how the generalized linear regression model (GLM) could be used to relate cyclists' safety with various contributing factors when riding in a mid-block bike lane. The types of 2-wheeled vehicles in the study included bicycle-style electric bicycles (BSEBs), scooter-style electric bicycles (SSEBs), and regular bicycles (RBs).



Traffic conflict technology was applied as a surrogate measure to evaluate the safety of 2-wheeled vehicles. The safety performance model was developed by adopting a generalized linear regression model for relating the frequency of rear-end conflicts between e-bikes and regular bikes to the operating speeds of BSEBs, SSEBs, and RBs in mid-block bike lanes.



The frequency of rear-end conflicts between e-bikes and bikes increased with an increase in the operating speeds of e-bikes and the volume of e-bikes and bikes and decreased with an increase in the width of bike lanes. The large speed difference between e-bikes and bikes increased the frequency of rear-end conflicts between e-bikes and bikes in mid-block bike lanes. A 1% increase in the average operating speed of e-bikes would increase the expected number of rear-end conflicts between e-bikes and bikes by 1.48%. A 1% increase in the speed difference between e-bikes and bikes would increase the expected number of rear-end conflicts between e-bikes/bikes by 0.16%.



The conflict frequency in mid-block bike lanes can be modeled using generalized linear regression models. The factors that significantly affected the frequency of rear-end conflicts included the operating speeds of e-bikes, the speed difference between e-bikes and regular bikes, the volume of e-bikes, the volume of bikes, and the width of bike lanes. The safety performance model can help better understand the causes of crash occurrences in mid-block bike lanes.
a Jiangsu Key Laboratory of Urban ITS, Jiangsu Collaborative Innovation Center of Modern Urban Traffic Technologies , Southeast University , Nanjing , China.
Lu
L
Bai
b California PATH, University of California at Berkeley , Richmond , California.
Ching-Yao
CY
Chan
a Jiangsu Key Laboratory of Urban ITS, Jiangsu Collaborative Innovation Center of Modern Urban Traffic Technologies , Southeast University , Nanjing , China.
Pan
P
Liu
a Jiangsu Key Laboratory of Urban ITS, Jiangsu Collaborative Innovation Center of Modern Urban Traffic Technologies , Southeast University , Nanjing , China.
Chengcheng
C
Xu
D000063:Accidents, Traffic / Q000706:statistics & numerical data; D001210:Asia, Southeastern; D001642:Bicycling; D004779:Environment Design / Q000706:statistics & numerical data*; D006801:Humans; D016014:Linear Models; D009049:Motorcycles; D012307:Risk Factors; D012449:Safety
BSEB; E-bike; SSEB; bike lane; generalized linear regression model; safety