Analysis of Freight Train Derailment Rates for Unit Trains and Manifest Trains
Zhipeng Zhang, Shanghai Jiao Tong UniversityShow Abstract
Xiang Liu, Rutgers University
Zheyong Bian, Rutgers University
Tyler Dick, University of Illinois, Urbana-Champaign
Jiaxi Zhao, University of Illinois, Urbana-Champaign
Steven Kirkpatrick, Applied Research Associates Inc
Freight rail is a safe and efficient mode of transporting hazardous materials. Railroad hazmat transportation safety is a high priority for the industry, academia, and government. In the past decade, the revenue ton-miles and car-miles of hazardous materials transported by unit trains have significantly increased in the United States. An understanding of the safety aspects of unit trains, in particular for hazmat unit trains, can contribute to the identification and evaluation of practical hazmat accident prevention strategies. However, limited prior research has focused on unit train derailment risk analysis. As part of this effort, this paper develops a quantitative analysis of freight unit train derailment characteristics and derailment rates for unit trains and manifest trains (mixed trains). The freight train derailment data on Class I railroad mainlines between 1996 and 2018 were analyzed by specific train types, including hazmat unit trains, non-hazmat unit trains and manifest trains. The derailment rates measured by three traffic exposure metrics, which are train-miles, ton-miles, and car-miles, are estimated and compared. The analyses show that a unit train has a 30% lower rate per billion ton-miles and per billion car-miles than manifest trains. Loaded unit trains have roughly four-fold derailment rate (in terms of derailments per million train-miles or per billion car-miles) of that of empty unit trains. Within loaded unit trains, hazmat unit trains have lower derailment rates than non-hazmat unit trains. Overall, heavier and shorter loaded unit trains tend to have grater derailment rates per billion ton-miles and per billion car-miles.
Recognition of Driving Fatigue Based on Support Vector Machine
Han-yan Zhu, Shanghai UniversityShow Abstract
Jin Luo, Shanghai University
Zhi-gang Liu, Shanghai University
Ting Gao, Shanghai University
This paper use support vector machine (SVM) to identify the fatigue of train drivers in rail transit. We designed a driving of urban rail transit train on the main line simulating experiment through the traffic simulation driving system. Select train drivers in good health condition as experimental subjects and collect pulse signals by Photoplethysmograph. Due to the single driving environment of rail transit train drivers, we proposed a fatigue classification standard based on the pupil area, recorded the eye-movement related data of the train drivers during the experiment by eye tracker equipment and identified the feasibility of the standard, the accuracy rate reached 86.36% by using SVM .
Identification of High-Risk Driving Behavior and Sections for Rail Systems
Yu-Fu Chen, National Taiwan UniversityShow Abstract
Kung-Chun Hsueh, National Taiwan University
Yung-Cheng Lai, National Taiwan University
Risk assessment is an important process for railway safety. Current practices for assessing the risks of driving behaviors aim to inspect the driving record generated by automatic train protection systems. This research proposes an automatic process to access detailed data contained in driving data, and identifies six high-risk driving behaviors. The modules can assess the competency of drivers and evaluate the frequency of high-risk behaviors in each section. Moreover, an integrated risk index for driving behaviors is proposed to compare each driver and section. An empirical study for drivers and sections are performed to demonstrate the feasibility of applying the proposed modules in practice. Results reveal that 20% of high-risk drivers contribute to 74% of the total risk, while 15% of high-risk sections contribute to 80% of the total risk. The proposed modules identify the drivers and sections with high risk to enable the operators of railway systems to take countermeasures, thereby enabling them to efficiently improve the safety of railway systems.
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