ABSTRACT: The use of electric bicycles (e-bikes) has growing rapidly in recent decades, resulting in a significant rise in e-bike crash rates. Therefore, improving the safety of e-bikes is essential to maintain the rapid growth of this sustainable mode of transport. One policy prescription is to focus on area-wide traffic safety management by identifying hot zones for e-bike crashes and their contributory factors. In macro-level safety modelling, the primary explanatory variable – the area of a zone, vary greatly in sizes. This phenomenon poses realistic challenges to the accuracy of macro-level statistical models. Yet, limited studies examined the influence of discrepancy in zone sizes on macro-level safety analysis related to e-bikes. To fill the research gap, this study aims to examine the influence of the size of an area on macro-level modelling. Spatial data on e-bike crash, road network, land use and socio-economic for 213 administrative units in Shanghai were collected. Then three Poisson log-normal Conditional Autoregressive models were developed with different modelling strategies to address the impact of area scale. In Model 1, area was modelled as a regular independent variable. While in Model 2, area was considered as an exposure variable. Finally, in Model 3, independent variables and dependent variable were divided by area. The results indicated Model 2 outperforms other two models. To identify hot zones, Potential for Safety Improvement estimates of three models were aggregated separately. The findings from this study can provide guidelines in considering the influence of area scale in macro-level modelling and hot-zone identification.
Xuesong Wang, Yongfeng Tang, Mohammed Quddus, Qingya Zhou, Xinyi Hou. Spatial Models for High-Accurate Hot Zone Identification for E-bikes. Transportation Research Board 100th Annual Meeting, Washington D.C., USA, 2021. 1.25-29.