Is it safe to cross? Identification of trains and their approach speed at level crossings

Larue, Grégoire S.; Filtness, Ashleigh J.; Wood, Joanne M.; Demmel, Sébastien; Watling, Christopher N.; Naweed, Anjum; Rakotonirainy, Andry

doi:10.1016/j.ssci.2017.11.009

Cited by 23 publications

(15 citation statements)

References 24 publications

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“…This was also observed by the research team on the multiple occasions they visited the site. This is in line with the literature, which shows that drivers tend to underestimate speeds of large objects such as trains (Clark, Perrone, & Isler, 2013; Larue et al, 2018), and report that they would enter level crossings even when trains are very close to level crossings when sighting distances are very long (Larue et al, 2018), as at the trial site considered in this study.…”

Section: Discussionsupporting

confidence: 92%

Validation of a Driving Simulator Study on Driver Behavior at Passive Rail Level Crossings

et al. 2018

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Section: Discussionsupporting

confidence: 92%

Validation of a Driving Simulator Study on Driver Behavior at Passive Rail Level Crossings

et al. 2018

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“…Most LCs around the world are passive. In Australia it is 67% of the total number of LCs and 75% in the USA [3]. In 2011, in the UK, 75% of all LCs were passive but in Belgium only 15% [4].…”

Section: Types Of Level Crossingsmentioning

confidence: 99%

“…Increased speeds of trains and vehicles on roads require greater sight distance for deciding whether to enter a LC or not. One of the main causes of crashes at LCs where there is a lack of barriers, is the behaviour of drivers who fail to judge correctly the speed and distance of approaching trains [3]. In 1996, the Transport Research Laboratory (UK) identified two categories of drivers that are most likely to be involved in accidents at LCs: drivers who believe that they have enough time to cross before train arrives, and do not stop or cannot stop because they are too close to the "stop line" when the amber light starts to flash, and drivers who are distracted or careless of the signals.…”

Section: Lc Accidents and Types Of Users Involvedmentioning

confidence: 99%

Investigation of Factors Influencing the Efficiency of Railways in Terms of Safety at Level Crossings

Watson

Ali

Bayyati

2020

WIT Transactions on the Built Environment

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Road traffic accidents are in the top ten of all deaths worldwide. Regarding railways, in the EU there is one person killed or seriously injured every day in serious accidents involving level crossings (LCs). The number of accidents is almost unchanged in the last ten years despite the huge efforts that railways have made. There is a need to look at the factors that contributed to the occurrence of accidents at LCs beyond the drivers and pedestrian behavior or technical issues. The reported research investigates the efficiency of railways in terms of accident risk at LCs in 24 countries of the EU. It evaluates the efficiency of railways by applying the Data Envelopment Analysis (DEA) method and identifying the factors that influence the efficiency score of railways in terms of accident risk using the IBM SPSS. The results show that GDP per capita and density of population in the selected countries have a strong influence on the efficiency of railways. The expected outcome of this research may contribute to better understanding of the factors that influence the efficiency score of railways in term of accident risk at LCs and develop preventative measures.

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“…With regard to highway-railway grade crossing safety issues, several studies discuss various aspects of crossing safety (Saccomanno et al, 2004;Millegan et al, 2009;Chaudhary et al, 2011;Chadwick et al 2014;Tung et al, 2015;Wang et al, 2016a;Heydari et al, 2016b;Haleem, 2016;Liu and Khattak, 2017;Hsu and Jones, 2017;Sperry et al, 2017;Zhang et al, 2017;Larue et al, 2018;Beanland et al, 2018). Many grade crossing studies can be divided into two categories: (1) crash-frequency studies (Hauer and Persaud, 1987;Austin and Carson, 2002;Saccomanno and Lai, 2005;Park and Saccomanno, 2005;Oh et al, 2006;Yan et al, 2010;Medina and Benekohal, 2015;Heydari and Fu, 2015;Lu and Tolliver, 2016;Heydari et al, 2016a;Heydari et al, 2017b; Guadamuz-Flores and Aguero-Valverde, 2017); and (2) crash-consequence studies (Eluru et al, 2012;Hao et al, 2015;Ghomi et al, 2016;Zhao et al, 2018).…”

Section: Unobserved Heterogeneitymentioning

confidence: 99%

Benchmarking regions using a heteroskedastic grouped random parameters model with heterogeneity in mean and variance: Applications to grade crossing safety analysis

Heydari

Thakali

et al. 2018

Analytic Methods in Accident Research

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Comparing regions while adjusting for differences in characteristics of sites located in those regions is valuable since it identifies possible interregional dissimilarities in crash risk propensities according to specific safety performance measures (e.g., crash frequency of a specific type). This paper describes a framework to benchmark different regions (neighborhoods, provinces, etc.) in terms of a selected safety performance measure. To avoid issues relating to aggregated (macro-level) data, we use disaggregate (micro-level) data to draw inferences at a macro/region-level, which is often needed for developing large-scale transportation safety and planning programs and policies. To overcome unobserved heterogeneity, we employ a multilevel Bayesian heteroskedastic Poisson lognormal model with grouped random parameters allowing heterogeneity in both mean and variance parameters. The proposed approach is illustrated through a comprehensive study of highway railway grade crossings across Canada. The results indicate that the proposed model addresses unobserved heterogeneity more efficiently and provides more insight compared to conventional random parameters models. For example, we found that as traffic exposure increases, grade crossing safety deteriorates at a higher rate in the Canadian Prairies than in the other regions. Our benchmarking framework is also affected by different model specifications. The results indicate the need for further indepth investigations, which could help to identify possible reasons for inter-region differences in terms of specific safety indicators. This study provides valuable guidelines to Canadian transportation authorities, revealing important underlying crash mechanisms at highway railway grade crossings in Canada.

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Is it safe to cross? Identification of trains and their approach speed at level crossings

Cited by 23 publications

References 24 publications

Validation of a Driving Simulator Study on Driver Behavior at Passive Rail Level Crossings

Validation of a Driving Simulator Study on Driver Behavior at Passive Rail Level Crossings

Investigation of Factors Influencing the Efficiency of Railways in Terms of Safety at Level Crossings

Benchmarking regions using a heteroskedastic grouped random parameters model with heterogeneity in mean and variance: Applications to grade crossing safety analysis

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