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Metro lines have undergone a rapid development in China and a large number of metro stations have also been built. The passenger traffic volume has reached or exceeded the designed transport capacity in some big cities such as Beijing and Shanghai. The safety evacuation problem within metro stations under emergency has become a worldwide concern. In this study, BuildingEXODUS was employed as the simulation platform and a metro station in Shanghai was selected for model development. Based on field survey data, the evacuation process in different fire cases was simulated, so as to evaluate the effects of two parameters (i.e., escalators and automatic ticket checkers) on evacuation performance. The research found that the use of two stopped escalators (normal metro stations have two) as fixed evacuation passages is effective and essential for safety evacuation. However, it surprisingly decreases the evacuation efficiency if using only one stopped escalator as the fixed evacuating passage. The evacuation efficiency can be improved by opening the automatic ticket checkers compared with maintaining normal status. Removing the automatic ticket checkers does not pose any difference in improving evacuation efficiency.

In this paper, the risk pattern of e-bike riders in China was examined, based on tree-structured machine learning techniques. Three-year crash/violation data were acquired from the Kunshan traffic police department, China. Firstly, high-risk (HR) electric bicycle (e-bike) riders were defined as those with at-fault crash involvement, while others (i.e. non-at-fault or without crash involvement) were considered as non-high-risk (NHR) riders, based on quasi-induced exposure theory. Then, for e-bike riders, their demographics and previous violation-related features were developed based on the crash/violation records. After that, a systematic machine learning (ML) framework was proposed so as to capture the complex risk patterns of those e-bike riders. An ensemble sampling method was selected to deal with the imbalanced datasets. Four tree-structured machine learning methods were compared, and a gradient boost decision tree (GBDT) appeared to be the best. The feature importance and partial dependence were further examined. Interesting findings include the following: (1) tree-structured ML models are able to capture complex risk patterns and interpret them properly; (2) spatial-temporal violation features were found as important indicators of high-risk e-bike riders; and (3) violation behavior features appeared to be more effective than violation punishment-related features, in terms of identifying high-risk e-bike riders. In general, the proposed ML framework is able to identify the complex crash risk pattern of e-bike riders. This paper provides useful insights for policy-makers and traffic practitioners regarding e-bike safety improvement in China.

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