Abstract
Conventional statistical models, such as Poisson or negative binomial, have predefined underlying relationships between explanatory variables. However, artificial neural network (ANN), which overcomes the limitations of statistical prediction model, have gained popularity in practice and research for their ability to increase prediction accuracy. Thus, this study employs zero-truncated negative binomial (ZTNB) models and artificial neural network (ANN) models to analyze the distribution of railroad accident frequency and the corresponding number of casualties for 1995–2021 accident dataset of Korea’s national railroad. The study mainly focused on two most dominant accident types which are human-involved accidents (accounted for 89.2% of all accidents) and ground-level crossing accidents (9.6%) from the historic dataset. This is because not just data proportion, rather such accident types were received very little attention compared to fatal train accident in the accident prediction study. Further, these types of accident showed clearly tended to decrease over time, but time trend has been found very weak at the type of fatal train accident. The performance of the developed models was estimated by mean square error (MSE), the root mean square error (RMSE), and the coefficient of determination (R2). Results present that ANN models outperform ZTNB models in fitting and prediction, demonstrating once again ANN’s superiority over statistical models for predicting accident frequency and casualty count.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abdelwahab HT, Abdel-Aty MA (2001) Development of artificial neural network models to predict driver injury severity in traffic accidents at signalized intersections. Transportation Research Record 1746:6–13, DOI: https://doi.org/10.3141/1746
Austin R, Carson J (2002) An alternative accident prediction model for highway-rail interfaces. Accident Analysis & Prevention 34(1):31–42, DOI: https://doi.org/10.1016/S0001-4575(00)00100-7
Cameron AC, Trivedi PK (1998) Regression analysis of count data. Cambridge University Press, Cambridge, United Kingdom
Chang L (2005) Analysis of freeway accident frequencies: Negative binomial regression versus artificial neural network. Safety Science 43(8):541–557, DOI: https://doi.org/10.1016/j.ssci.2005.04.004
Codur MY, Tortum A (2015) An artificial neural network model for highway accident prediction: A case study of Erzurum, Turkey. PROMET-Traffic & Transportation 27(3):217–225, DOI: https://doi.org/10.7307/ptt.v27i3.1551
Delen D, Sharda R, Bessonov M (2006) Identifying significant predictors of injury severity in traffic accident using a series of artificial neural networks. Accident Analysis & Prevention 38(3):434–444, DOI: https://doi.org/10.1016/j.aap.2005.06.024
Eluru N, Bagheri M, Miranda-Moreno LF, Fu L (2012) A latent class modeling approach for identifying vehicle driver injury severity factors at highway-railway crossings. Accident Analysis & Prevention 47:119–127, DOI: https://doi.org/10.1016/j.aap.2012.01.027
Evans AW (1997) A statistical analysis of fatal collisions and derailments of passenger trains on British railways: 1967–1996. Proceeding of the Institution of Mechanical Engineers 211(2):73–86, DOI: https://doi.org/10.1243/0954409971530923
Evans AW (2000) Fatal train accidents on Britain’s mainline railways. Journal of the Royal Statistical Society, Series A (Statistics in Society) 163(1):99–119, DOI: https://doi.org/10.1111/1467-985X.00159
Evans AW (2002) Speed and rolling stock of trains in fatal accidents on Britain’s mainline railways: 1967–2000. Proceeding of the Institution of Mechanical Engineers 216(F2):81–95, DOI: https://doi.org/10.1243/09544090260082326
Evans AW (2003) Estimating transport facility risk from past accident data. Accident Analysis & Prevention 35(4):459–472, DOI: https://doi.org/10.1016/S0001-4575(02)00024-6
Evans AW (2011) Fatal train accidents on Europe’s railways: 1980–2009. Accident Analysis & Prevention 43(1):391–401, DOI: https://doi.org/10.1016/j.aap.2010.09.009
Evans AW (2021) Fatal train accidents on Europe’s railways: An update to 2019. Accident Analysis & Prevention 158, DOI: https://doi.org/10.1016/j.aap.2021.106182
Fan W, Kane MR, Haile E (2015) Analyzing severity of vehicle crashes at highway-rail grade crossings: Multinomial logit modeling. Journal of the Transportation Research Forum 39–56, DOI: https://doi.org/10.22004/ag.econ.241825
Gao L, Lu P, Ren Y (2021) A deep learning approach for imbalanced crash data in predicting highway-rail grade crossings accidents. Reliability Engineering and System Safety 216, DOI: https://doi.org/10.1016/j.ress.2021.108019
Ghomi H, Bagheri M, Fu L, Miranda-Moreno LF (2016) Analyzing injury severity factors at highway railway grade crossing accidents involving vulnerable road users: A comparative study. Traffic Injury Prevention 17(8):833–841, DOI: https://doi.org/10.1080/15389588.2016.1151011
Grogger JT, Carson RT (1991) Models for truncated counts. Journal of Applied Econometrics 6(3):225–238, DOI: https://doi.org/10.1002/jae.3950060302
Gurmu S (1991) Tests for detecting over-dispersion in the positive Poisson regression model. Journal of Business and Economic Statistics 9(2):215–222, DOI: https://doi.org/10.1080/07350015.1991.10509847
Haghani S, Sedehi M, Kheiri S (2017) Artificial neural network to modeling zero-inflated count data: Application to predicting number of return to blood donation. Journal of Research in Health Science 17(3):392, PMCID: PMC7189957
Haleem K, Gan A (2015) Contributing factors of crash injury severity at public highway railroad grade crossings in the US. Journal of Safety Research 53:23–29, DOI: https://doi.org/10.1016/j.jsr.2015.03.005
Hao W, Daniel J (2014) Motor vehicle driver injury severity study under various traffic control at highway-rail grade crossings in the United States. J. Safety Res. 51:41–48, DOI: https://doi.org/10.1016/j.jsr.2014.08.002
Hao W, Daniel J (2016) Driver injury severity related to inclement weather at highway-rail grade crossings in the United States. Traffic Injury Prevention 17(1):31–38, DOI: https://doi.org/10.1080/15389588.2015.1034274
Hao W, Daniel JR (2013) Severity of injuries to motor vehicle drivers at highway-rail grade crossings in the United States. Transportation Research Record 2384(1):102–108, DOI: https://doi.org/10.3141/2384-12
Hauer E (2001) Overdispersion in modeling accidents on road sections and in empirical Bayes estimation. Accident Analysis & Prevention 33(6):799–808, DOI: https://doi.org/10.1016/S0001-4575(00)00094-4
Hilbe JM (2007) Negative binomial regression. Cambridge University Press, Cambridge, UK
Hu S, Li C, Lee C (2010) Investigation of key factors for accident severity at railroad grade crossings by using a logit model. Safety Science 48(2):186–194, DOI: https://doi.org/10.1016/j.ssci.2009.07.010
Hu S, Li C, Lee C (2012) Model crash frequency at highway-rail grade crossings using negative binomial regression. Journal of the Chinese Institute of Engineers 35(7):841–852, DOI: https://doi.org/10.1080/02533839.2012.708527
Iranitalab A, Khattak A (2017) Comparison of four statistical and machine learning methods for crash severity prediction. Accident Analysis & Prevention 108:27–36, DOI: https://doi.org/10.1016/j.aap.2017.08.008
Kang Y, Khattak A (2017) Cluster-based approach to analyzing crash injury severity at highway-rail grade crossings. Transportation Research Record 2608(1):58–69, DOI: https://doi.org/10.3141/2608-07
Liu X, Saat MR, Qin X, Barkan C (2013) Analysis of U.S. freight-train derailment severity using zero-truncated negative binomial regression and quantile regression. Accident Analysis & Prevention 59:87–93, DOI: https://doi.org/10.1016/j.aap.2013.04.039
Lord D, Mannering F (2010) The statistical analysis of crash-frequency data: A review and assessment of methodologies alternatives. Transportation Research Part A 44(5):291–305, DOI: https://doi.org/10.1016/j.tra.2010.02.001
Lord D, Washington SP, Ivan JN (2005) Poisson-gamma and zero inflated regression models of motor vehicle crashes: Balancing statistical fit and theory. Accident Analysis & Prevention 37(1):35–46, DOI: https://doi.org/10.1016/j.aap.2004.02.004
Lu P, Tolliver D (2016) Accident prediction model for public highway-rail grade crossings. Accident Analysis & Prevention 90(May):73–81, DOI: https://doi.org/10.1016/j.aap.2016.02.012
Ma C, Hao W, Xiang W, Yan W (2018) The impact of aggressive driving behavior on driver-injury severity at highway-rail grade crossings accidents. Journal of Advanced Transportation 2018, Article ID 9841498, DOI: https://doi.org/10.1155/2018/9841498
Miwa M, Gozun B, Oyama T (2006) Statistical data analyses to elucidate the causes and improve the countermeasures for preventing train accidents in Japan. International Transactions in Operational Research 13(3):229–251, DOI: https://doi.org/10.1111/j.1475-3995.2006.00546.x
Mussone L, Bassani M, Masci P (2017) Analysis of factors affecting the severity of crashes in urban road intersections. Accident Analysis & Prevention 103(June):112–122, DOI: https://doi.org/10.1016/j.aap.2017.04.007
Nielsen MA (2015) Neural networks and deep learning. Determination Press, 24–38
Oh A, Washington SP, Nam D (2006) Accident prediction model for railway-highway interfaces. Accident Analysis & Prevention 38(2):346–356, DOI: https://doi.org/10.1016/j.aap.2005.10.004
Rasaiah WG (2002) Watershed UK accidents-lessons learned. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 216(2):109–115, DOI: https://doi.org/10.1243/09544090260082344
Raub RA (2009) Examination of highway-rail grade crossing collisions nationally from 1998 to 2007. Transportation Research Record 2122(1):63–71, DOI: https://doi.org/10.3141/2122-08
Savolainen PT, Mannering FL, Lord D, Quddus MA (2011) The statistical analysis of highway crash-injury severities: A review and assessment of methodological alternatives. Accident Analysis & Prevention 43(5):1666–1676, DOI: https://doi.org/10.1016/j.aap.2011.03.025
Wood GR (2002) Generalised linear accident models and goodness of fit testing. Accident Analysis & Prevention 34(4):417–427, DOI: https://doi.org/10.1016/S0001-4575(01)00037-9
Wood GR (2005) Confidence and prediction intervals for generalised linear accident models. Accident Analysis & Prevention 37(2):267–273, DOI: https://doi.org/10.1016/j.aap.2004.10.005
Xie Y, Lord D, Zhang Y (2007) Predicting motor vehicle collision using Bayesian neural network models: An empirical analysis. Accident Analysis & Prevention 39(5):922–933, DOI: https://doi.org/10.1016/j.aap.2006.12.014.
Yang C, Trudel E, Liu Y (2017) Machine learning-based methods for analyzing grade crossing safety. Cluster Computing 20:1625–1635, DOI: https://doi.org/10.1007/s10586-016-0714-2
Ye Z, Xu Y, Veneziano D, Shi X (2014) Evaluation of winter maintenance chemicals and crashes with an artificial neural network. Transportation Research Record 2440(1):43–50, DOI: https://doi.org/10.3141/2440-06
Zeng Q, Huang H (2014) A stable and optimized neural network model for crash injury severity prediction. Accident Analysis & Prevention 73:351–358, DOI: https://doi.org/10.1016/j.aap.2014.09.006
Zheng Z, Lu P, Pan D (2019) Predicting highway-rail grade crossing collision risk by neural network systems. Journal of Transportation Engineering Part A Systems 145(8), DOI: https://doi.org/10.1061/JTEPBS.0000257
Zhang L, Meng X (2011) An approach to predict road accident frequencies: Application of fuzzy neural network. Retrieved March 9, 2022, https://onlinepubs.trb.org/onlinepubs/conferences/2011/RSS/2/Zheng,L.pdf
Zhang Z, Turla T, Liu X (2021) Analysis of human-factor-caused freight train accidents in the United States. Journal of Transportation Safety & Security 13(10):1157–1186, DOI: https://doi.org/10.1080/19439962.2019.1697774
Acknowledgments
This study was supported by research fund from the Songwon University.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Lim, KK. Analysis of Railroad Accident Prediction using Zero-truncated Negative Binomial Regression and Artificial Neural Network Model: A Case Study of National Railroad in South Korea. KSCE J Civ Eng 27, 333–344 (2023). https://doi.org/10.1007/s12205-022-1198-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12205-022-1198-7