Predicting the Severity of Accidents at Highway Railway Level Crossings of the Eastern Zone of Indian Railways using Logistic Regression and Artificial Neural Network Models
Received: 5 February 2024 | Revised: 17 February 2024 | Accepted: 4 March 2024 | Online: 3 April 2024
Corresponding author: Anil Kumar Chhotu
Abstract
Road-railroad level crossing accidents pose serious safety risks to road users, and their significant increase requires more research efforts to propose substitute solutions. Such a solution must consider the impact of intersection geometry, user perception, traffic characteristics, driver behavior, environment, and seasonal variations on accidents. This study explores the considerable number of such accidents and develops a predictive model using all the factors that influence them. For these objectives, data were collected from databases maintained by the zonal head office of the East Central Railway (ECR) in India. Data included 175 level crossings that experienced at least one accident between 2006 and 2021 in the ECR region. This study presents two accident prediction models using logistic regression and ANN for the predominant factors of accidents in the ECR zone of Indian railways. The accuracy of fatal accident prediction was 96% for logistic regression and 98% for ANN.
Keywords:
Railroad level crossing,ANN,Logistic regression,Prediction modelDownloads
References
"Railway Networks of India." knowIndia.net, http://knowindia.net/rail.html.
"Ministry of Railways (Railway Board)." https://indianrailways.gov.in/railwayboard/view_section.jsp?lang=0&id=0,1,304,366,554.
National Crime Records Bureau - Ministry of Home Affairs, "Accidental Deaths and Suicides in India 2022." https://data.opencity.in/dataset/accidental-deaths-and-suicides-in-india-2022.
K. Haleem and A. Gan, "Contributing factors of crash injury severity at public highway-railroad grade crossings in the U.S.," Journal of Safety Research, vol. 53, pp. 23–29, Jun. 2015.
K. Haleem, "Investigating risk factors of traffic casualties at private highway-railroad grade crossings in the United States," Accident Analysis & Prevention, vol. 95, pp. 274–283, Oct. 2016.
W. Hao, C. Kamga, and D. Wan, "The effect of time of day on driver’s injury severity at highway-rail grade crossings in the United States," Journal of Traffic and Transportation Engineering (English Edition), vol. 3, no. 1, pp. 37–50, Feb. 2016.
A. Naweed, "Psychological factors for driver distraction and inattention in the Australian and New Zealand rail industry," Accident Analysis & Prevention, vol. 60, pp. 193–204, Nov. 2013.
A. Silla and J. Luoma, "Main characteristics of train–pedestrian fatalities on Finnish railroads," Accident Analysis & Prevention, vol. 45, pp. 61–66, Mar. 2012.
G. C. Cothen, "Role of Human Factors in Rail Accidents | US Department of Transportation." https://www.transportation.gov/testimony/role-human-factors-rail-accidents.
S. A. Arhin and A. Gatiba, "Predicting Injury Severity of Angle Crashes Involving Two Vehicles at Unsignalized Intersections Using Artificial Neural Networks," Engineering, Technology & Applied Science Research, vol. 9, no. 2, pp. 3871–3880, Apr. 2019.
Y. Kassem, H. Camur, M. T. Adamu, T. Chikowero, and T. Apreala, "Prediction of Solar Irradiation in Africa using Linear-Nonlinear Hybrid Models," Engineering, Technology & Applied Science Research, vol. 13, no. 4, pp. 11472–11483, Aug. 2023.
A. Khattak and L.-W. Tung, "Severity of Pedestrian Crashes at Highway-Rail Grade Crossings," Journal of the Transportation Research Forum, vol. 54, no. 2, Jun. 2015.
F. Mlawa, E. Mkoba, and N. Mduma, "A Machine Learning Model for detecting Covid-19 Misinformation in Swahili Language," Engineering, Technology & Applied Science Research, vol. 13, no. 3, pp. 10856–10860, Jun. 2023.
P. M. Salmon, G. J. M. Read, N. A. Stanton, and M. G. Lenné, "The crash at Kerang: Investigating systemic and psychological factors leading to unintentional non-compliance at rail level crossings," Accident Analysis & Prevention, vol. 50, pp. 1278–1288, Jan. 2013.
G. J. M. Read, P. M. Salmon, and M. G. Lenné, "Sounding the warning bells: The need for a systems approach to understanding behaviour at rail level crossings," Applied Ergonomics, vol. 44, no. 5, pp. 764–774, Sep. 2013.
G. S. Larue, A. Naweed, and D. Rodwell, "The road user, the pedestrian, and me: Investigating the interactions, errors and escalating risks of users of fully protected level crossings," Safety Science, vol. 110, pp. 80–88, Dec. 2018.
A. Keramati, P. Lu, D. Tolliver, and X. Wang, "Geometric effect analysis of highway-rail grade crossing safety performance," Accident Analysis & Prevention, vol. 138, Apr. 2020, Art. no. 105470.
F. E. Harrell, Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis. New York, NY, USA: Springer, 2001.
T. Fawcett, "An introduction to ROC analysis," Pattern Recognition Letters, vol. 27, no. 8, pp. 861–874, Jun. 2006.
S. Eksteen and G. D. Breetzke, "Predicting the abundance of African horse sickness vectors in South Africa using GIS and artificial neural networks," South African Journal of Science, vol. 107, no. 7, pp. 1–8, Jan. 2011.
S. Menard, Applied Logistic Regression Analysis. Thousand Oaks, CA, USA: SAGE, 2002.
K. A. Pituch and J. P. Stevens, Applied Multivariate Statistics for the Social Sciences: Analyses with SAS and IBM’s SPSS, Sixth Edition. New York, NY, USA: Routledge, 2015.
T. Fawcett, "An introduction to ROC analysis," Pattern Recognition Letters, vol. 27, no. 8, pp. 861–874, Jun. 2006.
D. W. H. Jr and S. Lemeshow, Applied Logistic Regression. John Wiley & Sons, 2004.
M. A. Shahin, H. R. Maier, and M. B. Jaksa, "Predicting Settlement of Shallow Foundations using Neural Networks," Journal of Geotechnical and Geoenvironmental Engineering, vol. 128, no. 9, pp. 785–793, Sep. 2002.
Downloads
How to Cite
License
Copyright (c) 2024 Anil Kumar Chhotu, Sanjeev Kumar Suman
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
