Highway-rail grade crossing safety challenges for shared operations of high-speed passenger and heavy freight rail in the U.S.

Chadwick, Samantha G.; Zhou, Nanyan; Saat, Mohd Rapik

doi:10.1016/j.ssci.2014.03.003

Cited by 54 publications

(29 citation statements)

References 17 publications

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“…The accident prediction formula developed by the U.S. Department of Transportation (USDOT) in the early 1980s sought to overcome the limitations of earlier models (Chadwick et al, 2014). This comprehensive formula comprises three primary equations:…”

Section: Context and Related Workmentioning

confidence: 99%

Developing accident prediction model for railway level crossings

et al. 2018

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Railway level crossing (LX) safety continues to be one of the most critical issues for railways, despite an everincreasing focus on improving design and application practices. Accidents at European LXs account for about one-third of the entire railway accidents and result in more than 300 deaths every year in Europe. Due to the non-deterministic causes, the complex operation background and the lack of thorough statistical analysis based on accident/incident data, the risk assessment of LXs remains a challenging task. In the present paper, some LX accident prediction models are developed. Such models allow for highlighting the influence of the main impacting parameters, i.e., the average daily road traffic, the average daily railway traffic, the annual road accidents, the vertical road profile, the horizontal road alignment, the road width, the crossing length, the railway speed limit and the geographic region. The Ordinary Least-Squares (OLS) and Nonlinear Least-Squares (NLS) methods are employed to estimate the respective coefficients of variables in the prediction models, based on the LX accident/incident data. The validation and comparison process is performed through statistical means to examine how well the estimation of the models fits the reality. The outcomes of validation and comparison attest that the improved accident prediction model has statisticbased approbatory quality. Moreover, the improved accident prediction model combined with the NB distribution shows relatively high predictive accuracy of the probability of accident occurrence.

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Section: Context and Related Workmentioning

confidence: 99%

Developing accident prediction model for railway level crossings

et al. 2018

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“…Because of the friction of wheel/rail interface, the wear damage has become increasingly severe [1,2]. The increase of axle load leads to excessive wear of wheel/rail materials and remarkably decreases the service life of wheel/rail system.…”

Section: Introductionmentioning

confidence: 99%

Investigation on microstructure and wear characteristic of laser cladding Fe-based alloy on wheel/rail materials

Ding

Wang

et al. 2015

Wear

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“…One solution would be to install additional obstacle detectors in order to reopen the exit barriers and order the train to brake. Such a protection system has been tested in the U.S. (Chadwick et al, 2014). Obstacle detectors should be intelligent enough to identify the difference between a car moving slowly and one that is stationary (Silmon and Roberts, 2010).…”

Section: Discussionmentioning

confidence: 99%

Analyzing risky behavior of motorists during the closure cycle of railway level crossings

et al. 2018

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Level Crossing (LX) safety is one of the most critical issues for railways. Collisions between trains and motorized vehicles contribute most to LX accidents, while the risky behavior of motorists is the primary cause of such accidents. Therefore, motorist behavior at LXs is a safety concern that requires special attention care. The present study is a tentative to acquire a better understanding of risky motorist behavior at LXs. Namely, risk analysis of motorist behavior is performed based on field observation conducted at 11 automatic LXs (10 equipped with two half barriers (SAL2) and 1 equipped with four half barriers (SAL4)). We particularly focus on motorist behavior during the LX closure cycle when the barriers are closed down completely. Based on recorded measurements, the zigzag violation rate is analyzed with regard to the prolonged LX closure duration and LX location (railway station nearby or not), respectively. Then, some other features characterizing risky behavior are determined, such as troop phenomenon, etc. Besides, since our aim is to analyze motorist behavior at SAL2 LXs, one SAL4 LX was considered in our experiments to examine the distinction of motorist responses to SAL2 and SAL4 protection systems, so as to compare the efficiency of SAL2 and SAL4 LXs in terms of safety. In summary, the findings of our analysis offer a novel insight for interpreting significant aspects underlying motorist decision-making during the LX closure cycle and facilitate identifying technical solutions to improve LX safety.

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Highway-rail grade crossing safety challenges for shared operations of high-speed passenger and heavy freight rail in the U.S.

Cited by 54 publications

References 17 publications

Developing accident prediction model for railway level crossings

Developing accident prediction model for railway level crossings

Investigation on microstructure and wear characteristic of laser cladding Fe-based alloy on wheel/rail materials

Analyzing risky behavior of motorists during the closure cycle of railway level crossings

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