Journal of Transportation Safety and Security

To secure the city against the transmission of COVID-19, metro ridership control is an important task of the metro corporation on the premise of meeting the basic travel demand as far as possible. First off, this paper describes the influence mechanism of COVID-19 on metro ridership in Changsha, including an analysis into the correlation among policies, population, and metro ridership. Secondly, this paper verifies the influence of governmental macro-policy on population mobility and metro train working diagram, and thereby on metro ridership, based on the actual data during Jan 12th to May 6th, 2020 and year-ago data (2019). And then the Difference-in-Difference (DID) model is used to verify the effect of policies on metro ridership in Changsha. Results also show the effectiveness of policy chain on the limit of metro ridership, which bears a strong correlation to the number of confirmed COVID-19 cases. A linear regression prediction model is built to predict metro ridership based on cumulative net inflow of population index, metro carrying capacity and confirmed COVID-19 cases. This paper is expected to provide reference for Metro Corporation to control ridership on the premise of meeting the basic travel demand amid the explosive outbreak of the epidemics.

Wang Xiang

Li Chen

Bin Wang

Qingwan Xue

Wei Hao

Xuemei Liu

Journal of Transportation Safety & Security

Policies, population and impacts in metro ridership response to COVID-19 in Changsha

政策、人口与影响：长沙地铁客运量对新冠疫情的响应

Abstract Human factors are major causes of train accidents in the United States. Understanding the safety risk of these accidents can provide insights into safety evaluation and improvement. This paper focuses on analyzing the train derailments and collisions due to human factors using 2000–2016 accident data on mainlines from the US Federal Railroad Administration. This research methodology involves three main sections. First, we analyze the statistical trend of annual accident rates by accident type and year. Based on the cause-specific distribution of accident frequency, the major causes are determined for each common accident type such as derailments and collisions. Next, we calculate accident severity (e.g., derailed cars, casualties) due to each specific human-factor accident cause. Finally, we compute annual accident risk and cause-specific accident risk using mean and alternative risk measures. The detailed accident data analysis approach herein can also be adapted to other types of train accidents, in support of decisions for rail safety improvement. The analysis of human-factor-caused train accidents can provide key information for the development and evaluation of potential safety improvement strategies.

Zhipeng Zhang

Tejashree Turla

Xiang Liu

Analysis of human-factor-caused freight train accidents in the United States

美国人为因素导致的货运列车事故分析

The data from the years 2009 until 2015, collected by a scientific research team at the Hannover Medical School for the GIDAS project (German In-Depth Accident Study), were used as the study sample. Results: Injuries do not occur more often or more seriously in pedelec accidents than in conventional bicycle accidents. For accidents involving pedelec riders, almost exclusively injury severities of MAIS 1 and 2 (slight/moderate) can be observed and only one case of severe injury (MAIS 3), whereas for accidents with conventional bicyclists (Maximal Accident Injury Severity) MAIS 3+ occurs more often with 4.3%. The statistical analysis shows that head injuries occur more often in pedelec accidents (42.2%) compared to conventional bicycle accidents (35.7%). In addition, there are 21.8% more driving accidents (i.e., single-vehicle accidents such as collisions against objects or falls) involving pedelecs compared to 12.0% for conventional bicycle accidents. Finally, this study shows that pedelec riders tend to be much older than bicyclists, and the injury pattern of bicyclists and pedelec riders seems to depend on the age of the rider: with increasing age a rising risk of head injuries occurs and, therefore, the use of bicycle helmets is an important factor for injury prevention.

Dietmar Otte

Thorsten Facius

Accident typology comparisons between pedelecs and conventional bicycles

电动助力车与传统自行车事故类型比较

Subasish Das

Swastika Barua

Ahmed Hossain

Unraveling the complex relationship between weather conditions and traffic safety

揭示天气条件与交通安全之间的复杂关系

Richard Dzinyela

Jinli Liu

Bahar Dadashova

Chiara Silvestri-Dobrovolny

Understanding patterns of factor influences in motorcycle crashes with fixed objects

理解摩托车与固定物体碰撞中因素影响的模式

M. Ashifur Rahman

Rohit Chakraborty

Nurul-Haq Mohammed

Md. Mahmud Hossain

Siam Junaed

Identifying attribute associations in fatal speeding crashes using latent class clustering and association rule mining

使用潜在类聚类和关联规则挖掘识别致命超速事故中的属性关联

Huiying Wen

Kunhuo Huang

Zhaoliang Ma

Junda Huang

Determinants of driver injury severity in electric vehicle crashes: A random parameters binary probit model with heterogeneity in means and variances

电动汽车碰撞中驾驶员伤害严重程度的决定因素：均值和方差异质性的随机参数二元Probit模型

Shi Dong

Zhiwei Cui

Daniel (Jian) Sun

Liming Ye

Shengxuan Ding

How do environmental and road factors impact automated vehicle crashes? An evidence from ADAS and ADS

环境与道路因素如何影响自动驾驶车辆事故？来自ADAS和ADS的证据

Abbreviation
Journal Impact	2.54
Quartiles(Global)	TRANSPORTATION(Q3)

ISSN	1943-9962, 1943-9970
h-index	28

Publication Information	Publisher: Taylor and Francis Ltd.，Publishing cycle: ，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2009，Proportion of original research papers: 94.00%，Self Citation Rate:20.80%， Gold OA Rate: 1.94%

Journal of Transportation Safety and Security

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Share Submission Experience

Policies, population and impacts in metro ridership response to COVID-19 in Changsha

Analysis of human-factor-caused freight train accidents in the United States

Accident typology comparisons between pedelecs and conventional bicycles

Unraveling the complex relationship between weather conditions and traffic safety

Understanding patterns of factor influences in motorcycle crashes with fixed objects

Unraveling the complex relationship between weather conditions and traffic safety

Understanding patterns of factor influences in motorcycle crashes with fixed objects

Identifying attribute associations in fatal speeding crashes using latent class clustering and association rule mining

Determinants of driver injury severity in electric vehicle crashes: A random parameters binary probit model with heterogeneity in means and variances

How do environmental and road factors impact automated vehicle crashes? An evidence from ADAS and ADS