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Deep neural network-based predictive modeling of road accidents

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Abstract

This work proposes to use deep neural networks (DNN) model for prediction of road accidents. DNN consists of two or more hidden layers with large number of nodes. Accident data of non-urban sections of eight highways were collected from official records, and dataset consists of a total of 2680 accidents. The data of 16 explanatory variables related to road geometry, traffic and road environment were collected from official records as well as through field studies. Out of a total of 222 data points of accident frequency, 148 were used for training and remaining 74 to test the models. To compare the performance of DNN-based modeling approach, gene expression programming (GEP) and random effect negative binomial (RENB) models were used. A correlation coefficient value of 0.945 (root mean square error = 5.908) was achieved by DNN in comparison with 0.914 (RMSE = 7.474) by GEP, and 0.891 (RMSE = 8.862) by RENB with the test dataset, indicating an improved performance by DNN in prediction of road accidents. In comparison with DNN, though lower value of correlation coefficient was achieved by GEP model, it quantified the effects of various variables on accident frequency and provided a ranked list of variables based upon their importance.

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References

  1. MORTH (2017) Basic road statistics, 2017. Ministry of Road Transport and Highways, Government of India, India

  2. NIC (2017) State-wise distribution of accidental deaths by unnatural causes during 2017. National Informatics Centre Department of Electronics and Information Technology, Ministry of Communications and Information Technology, Government of India

  3. MORTH (2018) Road accidents in India, 2018. Ministry of Road Transport and Highways, Government of India, India

  4. Shaheem S, Mohammed KMS, Rajeevan (2006) Evaluation of cost effectiveness of improvements of accident prone locations on NH-47 in Kerala state. Indian Highw 34(2006):35–46

    Google Scholar 

  5. Elvik R (2011) Assessing causality in multivariate accident models. Accid Anal Prev 43:253–264

    Article  Google Scholar 

  6. Savolainen PT, Mannering FL, Lord D, Quddus MA (2011) The statistical analysis of highway crash-injury severities: a review and assessment of methodological alternatives. Accid Anal Prev 43(5):1666–1676

    Article  Google Scholar 

  7. Hauer E (2010) On prediction in road safety. Saf Sci. https://doi.org/10.1016/j.ssci.2010.03.003

    Article  Google Scholar 

  8. Lord D, Mannering F (2010) The statistical analysis of crash-frequency data: a review and assessment of methodological alternatives. Transp Res Part A 44:291–305

    Google Scholar 

  9. Mohan D, Tsimhoni O, Sivak M, Flannagan, MJ (2009) Road safety in India: challenges and opportunities. UMTRI, 1

  10. Mohan D (2002) Traffic safety and health in Indian Cities. J Transp Infrastruct 9(1):79–94

    Google Scholar 

  11. MORTH (2007) Sundar committee report, February, 2007. Ministry of Road Transport and Highways

  12. Sharma AK, Landge VS, Deshpande NV (2013) Modeling motorcycle accidents on rural highway. Int J Chem Environ Biol Sci 1(2):313–317

    Google Scholar 

  13. Jacob A, Anjaneyulu MVLR (2013) Development of crash prediction models for two-lane rural highways using regression analysis. Highw Res J 6(1):59–70

    Google Scholar 

  14. Fletcher JP, Baguley CJ, Sexton B, Done S (2006) Road accident modeling for highway development and management in developing countries. Main Report: Trials in India and Tanzania. Project Report No: PPR095, DFID

  15. Lord D, Geedipally SR, Persaud BN, Washington SP, Ivan JN, van Schalkwyk I, Lyon C, Jonsson T (2008) Methodology for estimating the safety performance of multilane rural highways. NCHRP Web-Only Document 126, National Cooperation Highway Research Program, Washington, DC. http://onlinepubs.trb.org/onlinepubs/nchrp/nchrpw126.pdf. Accessed 3 June 2010

  16. Cafiso S, Graziano AD, Silvestro GD, Cava GL, Persaud B (2010) Development of comprehensive accident models for two lane rural highways using exposure, geometry, consistency and context variables. Accid Anal Prev 42:1072–1079

    Article  Google Scholar 

  17. Hosseinpour M, Yahaya AS, Sadullah AF (2014) Exploring the effects of roadway characteristics on the frequency and severity of head-on crashes: case studies from Malaysian Federal. Accid Anal Prev 62:209–222

    Article  Google Scholar 

  18. Caliendo C, Guglielmo MLD, Guida M (2015) Comparison and analysis of road tunnel traffic accident frequencies and rates using random-parameter models. J Transp Saf Secur 8(2):177–195

    Article  Google Scholar 

  19. Kibar FT, Celik F, Wegman F (2017) Analyzing truck accident data on the interurban road Ankara–Aksaray–Eregli in Turkey: comparing the performances of negative binomial regression and the artificial neural networks models. J Transp Saf Secur. https://doi.org/10.1080/19439962.2017.1363841

    Article  Google Scholar 

  20. Abusini S, Kilicman A (2017) Modeling motorcycle accident on the road section by using general linear model: case studies in Batu City. Cogent Math 4(1):1379675. https://doi.org/10.1080/23311835.2017.1379675

    Article  Google Scholar 

  21. Gianfranco F, Soddu S, Fadda P (2018) An accident prediction model for urban road networks. J Transp Saf Secur 10(4):387–405. https://doi.org/10.1080/19439962.2016.1268659

    Article  Google Scholar 

  22. Xie Y, Lord D, Zhang Y (2007) Predicting motor vehicle collisions using Bayesian Neural Network models: an empirical analysis. Accid Anal Prev 39:922–933

    Article  Google Scholar 

  23. Driss M, Benabdeli K, Hamadouche T, Saint-Gerand MA (2015) Traffic safety prediction model for identifying spatial degrees of exposure to the risk of road accidents based on fuzzy logic approach. Geocarto Int 30(3):243–257. https://doi.org/10.1080/10106049.2014.883554

    Article  Google Scholar 

  24. Singh G, Sachdeva SN, Pal M (2018) Comparison of three parametric and machine learning approaches for modeling accident severity on non-urban sections of Indian highways. Adv Transp Stud Int J Sect B 45:123–140

    Google Scholar 

  25. Chang LY, Wang HW (2006) Analysis of traffic injury severity: an application of non-parametric classification tree techniques. Accid Anal Prev 38(5):1019–1027

    Article  Google Scholar 

  26. Singh G, Sachdeva SN, Pal M (2016) M5 model tree based predictive modeling of road accidents on non-urban sections of highways in India. Accid Anal Prev 96:108–117

    Article  Google Scholar 

  27. Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117

    Article  Google Scholar 

  28. Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13(2):87–129

    MathSciNet  MATH  Google Scholar 

  29. Zhang Z, Wang Y, Chen P, Yu G (2018) Application of long short-term memory neural network for multi-step travel time forecasting on urban expressways. In: CICTP 2017: transportation reform and change—equity, inclusiveness, sharing, and innovation. American Society of Civil Engineers, Reston, pp 444–454

  30. Zhou L, Chen X (2018) Short-term forecasting of traffic flow and speed: a deep learning approach. In: CICTP 2018: intelligence, connectivity, and mobility. American Society of Civil Engineers, Reston, VA, pp 2186–2196

  31. Deng F, He Y, Zhou S, Yu Y, Cheng H, Wu X (2018) Compressive strength prediction of recycled concrete based on deep learning. Constr Build Mater 175:562–569

    Article  Google Scholar 

  32. Kumar SS, Abraham DM (2019) A deep learning based automated structural defect detection system for sewer pipelines. In: Computing in civil engineering 2019: smart cities, sustainability, and resilience. American Society of Civil Engineers, Reston, VA, pp 226–233

  33. Dick K, Russell L, SouleyDosso Y, Kwamena F, Green JR (2019) Deep learning for critical infrastructure resilience. J Infrastruct Syst 25(2):05019003

    Article  Google Scholar 

  34. Ding F, Zhang Z, Zhou Y, Chen X, Ran B (2019) Large-scale full-coverage traffic speed estimation under extreme traffic conditions using a big data and deep learning approach: case study in China. J Transp Eng Part A Syst 145(5):05019001

    Article  Google Scholar 

  35. Nguyen T, Kashani A, Ngo T, Bordas S (2019) Deep neural network with high-order neuron for the prediction of foamed concrete strength. Comput Aided Civil Infrastruct Eng 34(4):316–332

    Article  Google Scholar 

  36. Yu P, Yan X (2019) Stock price prediction based on deep neural networks. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04212-x

    Article  Google Scholar 

  37. Zhou Y, Zhang L, Yi Z (2019) Predicting movie box-office revenues using deep neural networks. Neural Comput Appl 31:1855. https://doi.org/10.1007/s00521-017-3162-x

    Article  Google Scholar 

  38. Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436

    Article  Google Scholar 

  39. Nazari A (2019) Predicting the total specific pore volume of geo-polymers produced from waste ashes by gene expression programming. Neural Comput Appl 31(2):751. https://doi.org/10.1007/s00521-012-1135-7

    Article  MathSciNet  Google Scholar 

  40. Gholami A, Bonakdari H, Zeynoddin M et al (2019) Reliable method of determining stable threshold channel shape using experimental and gene expression programming techniques. Neural Comput Appl 31:5799. https://doi.org/10.1007/s00521-018-3411-7

    Article  Google Scholar 

  41. Malik H, Mishra S (2016) Application of gene expression programming (GEP) in power transformers fault diagnosis using DGA. IEEE Trans Ind Appl 52(6):4556–4565

    Article  Google Scholar 

  42. Dindarloo SR, Pollard JP, Siami-Irdemoosa E (2016) Off-road truck-related accidents in US mines. J Saf Res 58:79–87

    Article  Google Scholar 

  43. Yaacob W, Lazim M, Wah Y (2010) Evaluating spatial and temporal effects of accidents likelihood using random effects panel count model. In: Proceedings of the 2010 international conference on science and social research, Kuala Lumpur, Malaysia

  44. GENexpro (5.0 trial version) https://www.gepsoft.com/. Accessed 21 Feb 2018

  45. IBM Corp. Released 2013. IBM SPSS Statistics for Windows, Version 22.0. IBM Corp., Armonk, NY

  46. Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, USA

    MATH  Google Scholar 

  47. Nair V, Hinton GE (2010) Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th international conference on machine learning (ICML-10), Haifa, Israel, June 21–24, pp 807–814

  48. Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp 249–256

  49. Kingma DP, Ba J (2015) Adam: a method for stochastic optimization. In: 3rd international conference on learning representations, May 7–9, 2015, San Diego, https://arxiv.org/abs/1412.6980. Accessed 15 June 2019

  50. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958

    MathSciNet  MATH  Google Scholar 

  51. Agresti A, Booth JG, Caffo B (2000) Random-effects modeling of categorical response data. Sociol Methodol 30:27–80

    Article  Google Scholar 

  52. Dinu RR, Veeraragavan A (2011) Random parameter models for accident prediction on two-lane undivided highways in India. J Saf Res 42(1):39–42

    Article  Google Scholar 

  53. Quimby A, Maycock G, Palmer C, Buttress S (1999) The factors the influence a driver’s choice of speed: a questionnaire study. Transport Research Laboratory, Crowthorne, UK

    Google Scholar 

  54. Taylor MC, Baruya A, Kennedy JV (2002) The relationship between speed and accidents on rural single-carriageway roads, vol 511. TRL, Crowthorne

    Google Scholar 

  55. Elvik R, Vaa T (2004) The handbook of road safety measures. Elsevier, Amsterdam

    Google Scholar 

  56. Hauer E (2000) The median and safety. www.trafficsafetyresearch.com (unpublished)

  57. Ackaah W, Salifu M (2011) Crash prediction model for two-lane rural highways in the Ashanti region of Ghana. IATSS Res 35(1):34–40

    Article  Google Scholar 

  58. Harwood DW, Council FM, Hauer E, Hughes WE, Vogt A (2000) Prediction of the expected safety performance of rural two-lane highways (No. FHWA-RD-99-207, MRI 4584-09, Technical Report). Federal Highway Administration, United States

  59. Fitzpatrick K, Lord D, Park BJ (2010) Horizontal curve accident modification factor with consideration of driveway density on rural four-lane highways in Texas. J Transp Eng 136(9):827–835

    Article  Google Scholar 

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Authors and Affiliations

  1. Civil Engineering Department, DeenbandhuChhotu Ram University of Science and Technology, Murthal, Sonepat, Haryana, India

    Gyanendra Singh

  2. Department of Civil Engineering, National Institute of Technology, Kurukshetra, Haryana, India

    Mahesh Pal, Yogender Yadav & Tushar Singla

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  1. Gyanendra Singh
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Singh, G., Pal, M., Yadav, Y. et al. Deep neural network-based predictive modeling of road accidents. Neural Comput & Applic 32, 12417–12426 (2020). https://doi.org/10.1007/s00521-019-04695-8

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