A Comprehensive Assessment of the Existing Accident and Hazard Prediction Models for the Highway-Rail Grade Crossings in the State of Florida

Accidents at highway-rail grade crossings can cause fatalities and injuries, as well as significant property damages. In order to prevent accidents, certain upgrades need to be made at highway-rail grade crossings. However, due to limited monetary resources, only the most hazardous highway-rail grade crossings should receive a priority for upgrading. Hence, accident/hazard prediction models are required to identify the most hazardous highway-rail grade crossings for safety improvement projects. This study selects and evaluates the accident and hazard prediction models found in the highway-rail grade crossing safety literature to rank the highway-rail grade crossings in the State of Florida. Three approaches are undertaken to evaluate the candidate accident and hazard prediction models, including the chi-square statistic, grouping of crossings based on the actual accident data, and Spearman rank correlation coefficient. The analysis was conducted for the 589 highway-rail grade crossings located in the State of Florida using the data available through the highway-rail grade crossing inventory database maintained by the Federal Railroad Administration. As a result of the performed analysis, a new hazard prediction model, named as the Florida Priority Index Formula, is recommended to rank/prioritize the highway-rail grade crossings in the State of Florida. The Florida Priority Index Formula provides a more accurate ranking of highway-rail grade crossings as compared to the alternative methods. The Florida Priority Index Formula assesses the potential hazard of a given highway-rail grade crossing based on the average daily traffic volume, average daily train volume, train speed, existing traffic control devices, accident history, and crossing upgrade records.

[1]  A S Hakkert,et al.  The evaluation of road-rail crossing safety with limited accident statistics. , 1996, Accident; analysis and prevention.

[2]  Denver Tolliver,et al.  Accident prediction model for public highway-rail grade crossings. , 2016, Accident; analysis and prevention.

[3]  Doohee Nam,et al.  Accident prediction model for railway-highway interfaces. , 2006, Accident; analysis and prevention.

[4]  Chi-Kang Lee,et al.  Investigation of Key Factors for Accident Severity at Railroad Grade Crossings by Using a Logit Model. , 2010, Safety science.

[5]  Shannon C Mok,et al.  Why Has Safety Improved at Rail-Highway Grade Crossings? , 2005, Risk analysis : an official publication of the Society for Risk Analysis.

[6]  Jeffery E Warner,et al.  Evaluation of Grade Crossing Hazard Ranking Models , 2017 .

[7]  Mohd Rapik Saat,et al.  Highway-Rail Grade Crossing Safety Challenges for Shared Operations of High-Speed Passenger and Heavy Freight Rail in the U.S. , 2014 .

[8]  Attila Borsos,et al.  Safety Ranking of Railway Crossings in Hungary , 2016 .

[9]  Liping Fu,et al.  Estimating countermeasure effects for reducing collisions at highway-railway grade crossings. , 2007, Accident; analysis and prevention.

[10]  M. McHugh,et al.  The Chi-square test of independence , 2013, Biochemia medica.

[11]  Xuedong Yan,et al.  Using hierarchical tree-based regression model to predict train-vehicle crashes at passive highway-rail grade crossings. , 2010, Accident; analysis and prevention.

[12]  Gregoire S. Larue,et al.  Human Factors Evaluation of a Novel Australian Approach for Activating Railway Level Crossings , 2015 .

[13]  Chaozhong Wu,et al.  Driver injury severity study for truck involved accidents at highway-rail grade crossings in the United States , 2016 .

[14]  Christina A. Christie,et al.  The Chi-Square Test , 2012 .

[15]  Janice Daniel,et al.  Motor vehicle driver injury severity study under various traffic control at highway-rail grade crossings in the United States. , 2014, Journal of safety research.

[16]  Andrew W Evans.  Fatal accidents at railway level crossings in Great Britain 1946-2009. , 2011, Accident; analysis and prevention.

[17]  Christian Wullems,et al.  Towards the adoption of low-cost rail level crossing warning devices in regional areas of Australia : a review of current technologies and reliability issues , 2011 .

[18]  Neville A Stanton,et al.  To stop or not to stop: Contrasting compliant and non-compliant driver behaviour at rural rail level crossings. , 2017, Accident; analysis and prevention.

[19]  Jodi L Carson,et al.  An alternative accident prediction model for highway-rail interfaces. , 2002, Accident; analysis and prevention.

[20]  Sirkku Laapotti,et al.  Comparison of fatal motor vehicle accidents at passive and active railway level crossings in Finland , 2016 .

[21]  Liping Fu,et al.  Risk-Based Model for Identifying Highway-Rail Grade Crossing Blackspots , 2004 .

[22]  John O Sobanjo,et al.  Development of Algorithms for Effective Resource Allocation among Highway–Rail Grade Crossings: A Case Study for the State of Florida , 2020 .

[23]  Anjum Naweed,et al.  The road user, the pedestrian, and me: Investigating the interactions, errors and escalating risks of users of fully protected level crossings , 2018 .

[24]  Neville A. Stanton,et al.  Challenging conventional rural rail level crossing design: Evaluating three new systems thinking-based designs in a driving simulator , 2018, Safety Science.

[25]  Yubian Wang,et al.  Effects of foggy conditions on driver injury levels in U.S. highway-rail grade crossing accidents , 2017 .

[26]  EunSu Lee,et al.  Developing a Highway Rail Grade Crossing Accident Probability Prediction Model: A North Dakota Case Study , 2018 .

[27]  E. C. Wigglesworth,et al.  AN EVALUATION OF THE RAILWAY LEVEL CROSSING BOOM BARRIER PROGRAM IN VICTORIA, AUSTRALIA , 1991 .

[28]  G. Van Belle,et al.  Influencing factors for railroad-highway grade crossing accidents in Florida , 1975 .