a Jiangsu Key Laboratory of Urban ITS, Jiangsu Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University , Si Pai Lou #2, Nanjing , China , 210096.
OBJECTIVE: Electric bikes (e-bikes) were one of the fastest growing trip modes in Southeast Asia over the past two decades. The increasing popularity of e-bikes raised some safety concerns on urban transport system. The primary objective of this study was to identify whether and how the generalized linear regression model (GLM) could be used to relate the cyclists' safety with various contributing factors when they were riding in a mid-block bike lane. The types of two-wheeled vehicles in the study included bicycle style electric bicycles (BSEBs), scooter style electric bicycles (SSEBs) and regular bicycles (RBs).
METHODS: Traffic conflict technology was applied as a surrogate measures for evaluating the safety of two-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.
RESULTS: 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, while decreased with an increase in the width of bike lanes. 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. One percent 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%. One percent increase in the speed difference of e-bikes and bikes would increase the expected number of rear-end conflicts between e-bikes/bikes by 0.16%.
CONCLUSIONS: 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 of the 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.
Language: en
Keywords
BSEB; SSEB; bike lane; e-bike; generalized linear regression model; safety