Crash Density and Severity Prediction Using Recurrent Neural Networks Combined with Particle Swarm Optimization

  • Xinxin Xu
  • Ziqiang ZengEmail author
  • Yinhai Wang
  • John Ash
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1001)


Predicting traffic crashes has been an important topic of traffic safety research for the past many years. This paper investigates the data from police crash reports provided by the Washington State Department of Transportation. The data consists of records of four years from January 2011 to December 2014 for three main interstate highways (including I-5, I-90, and I-405). A deep learning model using a recurrent neural network (RNN) combined with particle swarm optimization (PSO) is developed and employed to predict the crash density in different severity levels such as property damage only (PDO) and fatal-injury crashes, based on 48,154 crash records that have occurred. All the crash records are randomly divided into training set, validation set, and test set with the proportion ratio of 70, 15, and \(15\%\). The cross-validation is employed to prevent the model from over fit during the training period. A normalized probability-based PSO is designed for optimizing the identified significant factors which can improve the prediction accuracy. The weighted mean squared error (MSE) of the prediction result is employed to measure the performance of the developed model. Nine explanatory variables are selected from fifteen contributing factors. The proposed model is compared with generalized nonlinear model-based mixed multinomial logit approach (GNM-based mixed MNL). The results show that the new model has lower fatal-injury and PDO MSEs. Sensitivity analysis on the selected variables demonstrates the capability of the new model for generating interpretable parameters. The findings of this study provide new insights into the prediction of crash density and severity from the perspective of using roadway segment-based crash records.


Crash density and severity Deep learning Recurrent neural network Particle swarm optimization Prediction 



This research was supported by the Youth Program of National Natural Science Foundation of China (Grant No. 71501137), the Humanities and Social Sciences Programs of Ministry of Education in China (Grant No. 17XJC790016), the project of Research Center for System Sciences and Enterprise Development (Grant No. Xq16B05), Sichuan University (Grant No. skqy201647), Soft Science Project of Sichuan Science and Technology Department (Grant No. 2019JDR0167), and Sichuan Social Science Planning Project (Grant No. SC18TJ014). The authors would like to give our great appreciation to the editors and anonymous referees for their helpful and constructive comments and suggestions, which have helped to improve this article.


  1. 1.
    Sameen, M., Pradhan, B.: Severity prediction of traffic accidents with recurrent neural networks. Appl. Sci. Basel 7(6):AN, 476 (2017)Google Scholar
  2. 2.
    World Health Organization: Global status report on road safety. (2015)
  3. 3.
    Iranitalab, A., Khattak, A.: Comparison of four statistical and machine learning methods for crash severity prediction. Accid. Anal. Prev. 108, 27–36 (2017)CrossRefGoogle Scholar
  4. 4.
    Fan, W.D., Gong, L., Washing, E.M., Yu, M., Haile, E.: Identifying and quantifying factors affecting vehicle crash severity at highway-rail grade crossings: models and their comparison. Transportation Research Board 95th Annual Meeting, Washington DC (2016)Google Scholar
  5. 5.
    Ahmed, M., Franke, R., Ksaibati, K., Shinstine, D.: Effects of truck traffic on crash injury severity on rural highways in Wyoming using Bayesian binary logit models. Accid. Anal. Prev. 117, 106–113 (2018)CrossRefGoogle Scholar
  6. 6.
    Ye, F., Lord, D.: Comparing three commonly used crash severity models on sample size requirements: multinomial logit, ordered probit and mixed logit models. Accid. Anal. Prev. 1, 72–85 (2014)Google Scholar
  7. 7.
    Zeng, Z., Zhu, W., Ke, R., Ash, J., Wang, Y., Xu, J., Xu, X.: A generalized nonlinear model-based mixed multinomial logit approach for crash data analysis. Accid. Anal. Prev. 99, 51–65 (2017)CrossRefGoogle Scholar
  8. 8.
    Lee, J., Yasmin, S., Eluru, N., Abdel-Aty, M., Cai, Q.: Analysis of crash proportion by vehicle type at traffic analysis zone level: a mixed fractional split multinomial logit modeling approach with spatial effects. Accid. Anal. Prev. 111, 12–22 (2018)CrossRefGoogle Scholar
  9. 9.
    Yasmin, S., Eluru, N.: Evaluating alternate discrete outcome frameworks for modeling crash injury severity. Accid. Anal. Prev. 59, 506–521 (2013)CrossRefGoogle Scholar
  10. 10.
    Patil, S., Geedipally, S., Lord, D.: Analysis of crash severities using nested logit model-accounting for the underreporting of crashes. Accid. Anal. Prev. 45, 646–653 (2012)CrossRefGoogle Scholar
  11. 11.
    Jalayer, M., Shabanpour, R., Pour-Rouholamin, M., Golshani, N., Zhou, H.: Wrong-way driving crashes: a random-parameters ordered probit analysis of injury severity. Accid. Anal. Prev. 117, 128–135 (2018)CrossRefGoogle Scholar
  12. 12.
    Fountas, G., Anastasopoulos, P., Mannering, F.: Analysis of vehicle accident-injury severities: a comparison of segment-versus accident-based latent class ordered probit models with class-probability functions. Anal. Methods Accid. Res. 18, 15–32 (2018)CrossRefGoogle Scholar
  13. 13.
    Abdelwahab, H., Abdel-Aty, M.: Development of artificial neural network models to predict driver injury severity in traffic accidents at signalized intersections. Transp. Res. Rec. J. Transp. Res. Board 1746, 6–13 (2001)CrossRefGoogle Scholar
  14. 14.
    Taamneh, M., Taamneh, S., Alkheder, S.: Clustering-based classification of road traffic accidents using hierarchical clustering and artificial neural networks. Int. J. Inj. Control Saf. Promot. 24(3), 388–395 (2017)CrossRefGoogle Scholar
  15. 15.
    Zheng, Z., Lu, P., Tolliver, D.: Decision tree approach to accident prediction for highway-rail grade crossings empirical analysis. Transp. Res. Rec. 2545, 115–122 (2016)CrossRefGoogle Scholar
  16. 16.
    Abellan, J., Lopez, G., de Ona, J.: Analysis of traffic accident severity using decision rules via decision trees. Expert Syst. Appl. 40(15), 6047–6054 (2013)CrossRefGoogle Scholar
  17. 17.
    Delen, D., Sharda, R., Bessonov, M.: Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks. Accid. Anal. Prev. 38(3), 434–444 (2006)CrossRefGoogle Scholar
  18. 18.
    Zeng, Q., Huang, H.: A stable and optimized neural network model for crash injury severity prediction. Accid. Anal. Prev. 73, 351–358 (2014)CrossRefGoogle Scholar
  19. 19.
    Huang, W., Song, G., Hong, H., Xie, K.: Deep architecture for traffic flow prediction: deep belief networks with multitask learning. IEEE Trans. Intell. Transp. Syst. 15, 2191–2201 (2014)CrossRefGoogle Scholar
  20. 20.
    Xie, Z., Yan, J.: Detecting traffic accident clusters with network kernel density estimation and local spatial statistics: an integrated approach. J. Transp. Geogr. 31, 64–71 (2013)CrossRefGoogle Scholar
  21. 21.
    Omar, T., Eskandarian, A., Bedewi, N.: Vehicle crash modelling using recurrent neural networks. Math. Comput. Model. 28(9), 31–42 (1998)CrossRefGoogle Scholar
  22. 22.
    Wijnands, J., Thompson, J., Aschwanden, G.D., Stevenson, M.: Identifying behavioural change among drivers using long short-term memory recurrent neural networks. Transp. Res. Part F Traffic Psychol. Behav. 53, 34–49 (2018)CrossRefGoogle Scholar
  23. 23.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997)CrossRefGoogle Scholar
  24. 24.
    Donahue, J., Anne Hendricks, L., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., Darrell, T.: Long-term recurrent convolutional networks for visual recognition and description. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, vol. 7–15, pp. 2625–2634 (2015)Google Scholar
  25. 25.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In Proceedings of the IEEE International Conference on Neural Networks (ICNN 95), Perth, Australia, 27 Nov–1 Dec, no. 1–6, pp. 1942–1948 (1995)Google Scholar
  26. 26.
    Zeng, Z., Xu, J., Wu, S., Shen, M.: Antithetic method-based particle swarm optimization for a queuing network problem with fuzzy data in concrete transportation systems. Comput. Aided Civ. Infrastruct. Eng. 29, 771–800 (2014)CrossRefGoogle Scholar
  27. 27.
    Gang, R., Zhou, Z.: Traffic safety forecasting method by particle swarm optimization and support vector machine. Expert Syst. Appl. 38(8), 10420–10424 (2011)CrossRefGoogle Scholar
  28. 28.
    Yang, J., Zhou, J., Liu, L., Li, Y.: A novel strategy of pareto-optimal solution searching in multi-objective particle swarm optimization (MOPSO). Comput. Math. Appl. 57(11–12), 1995–2000 (2009)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Everson, J.E., Fieldsend, J.E., Singh, S.: Using unconstrainted elite archives for multi-objective optimization. IEEE Trans. Evol. Comput. 7, 305–323 (2003)CrossRefGoogle Scholar
  30. 30.
    Mostaghim, S., Teich, J.: Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO). In: IEEE 2003 Swarm Intelligence Symposium, pp. 26–33 (2003)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Xinxin Xu
    • 1
  • Ziqiang Zeng
    • 2
    Email author
  • Yinhai Wang
    • 3
  • John Ash
    • 3
  1. 1.Business SchoolChengdu UniversityChengduPeople’s Republic of China
  2. 2.Business SchoolSichuan UniversityChengduPeople’s Republic of China
  3. 3.Smart Transportation Applications and Research Laboratory, Department of Civil and Environmental EngineeringUniversity of WashingtonSeattleUSA

Personalised recommendations