Skip to main content
SpringerLink
Log in

Machine Learning Prediction of Weather-Induced Road Crash Events for Experienced and Novice Drivers: Insights from a Driving Simulator Study

  • Conference paper
  • First Online:
Business Intelligence (CBI 2023)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 484 ))

Included in the following conference series:

Abstract

Road crashes are one of the most critical issues that pose a serious threat to our daily life; Crash occurrences prediction is a key role in designing efficient intelligent transportation systems. In this study, we aim to analyze road crash events for experienced and novice drivers under several weather conditions during multiple driving simulations that have been conducted using a desktop driving simulator. This work outlined the effect of snow and rain conditions on driver behavior by endorsing real-time driver data namely: wheel angle position, throttle pedal position and brake pedal position. Moreover, optimized modeling strategies using the deep learning algorithm Multilayer Perceptron (MLP) and Support Vector Machine (SVM) along with Bayesian Networks (BN) models have been developed to analyze crash events. To the authors’ knowledge, there has been a limited interest at assessing the impact of both snow and rainy weather conditions on the occurrence of crash events while providing a critical analysis for experienced and novice drivers based on driver entries; this approach fill the research gap of the combined effect of driving experience and weather conditions on road crash occurrence. The findings depict superior performances have been obtained when adopting the proposed strategy. As a whole, new insights into weather-induced crash events’ investigation for experienced and novice drivers have been acquired and can be endorsed for designing effective crash avoidance/warning systems.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 54.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 69.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. World Health Organization. WHO | Road Safety (2015). http://www.who.int/features/factfiles/roadsafety/en/

  2. Zouhair, E.A.E., Mousannif, H., Al Moatassime, H.: Towards analyzing crash events for novice drivers under reduced-visibility settings: a simulator study. In: Proceedings of the 3rd International Conference on Networking, Information Systems & Security (2020)

    Google Scholar 

  3. Horswill, M.S., Hill, A., Buckley, L., Kieseker, G., Elrose, F.: Further down the road: the enduring effect of an online training course on novice drivers’ hazard perception skill. Transp. Res. part F traffic Psychol. Behav. 94, 398–412 (2023)

    Article  Google Scholar 

  4. Ali, E.M., Ahmed, M.M., Wulff, S.S.: Detection of critical safety events on freeways in clear and rainy weather using SHRP2 naturalistic driving data: Parametric and non-parametric techniques. Saf. Sci., no. January 2018, (2019)

    Google Scholar 

  5. Yu, R., Zheng, Y., Abdel-Aty, M., Gao, L Exploring crash mechanisms with microscopic traffic flow variables: a hybrid approach with latent class logit and path analysis models. Accid. Anal. Prev., 125, pp. 70–78 (2019)

    Google Scholar 

  6. Jahangiri, A., Berardi, V.J., MacHiani, S.G.: Application of real field connected vehicle data for aggressive driving identification on horizontal curves. IEEE Trans. Intell. Transp. Syst. 19(7), 2316–2324 (2018)

    Article  Google Scholar 

  7. Abou Elassad, Z.E., Mousannif, H., Al, H., Karkouch, A.: The application of machine learning techniques for driving behavior analysis : a conceptual framework and a systematic literature review. Eng. Appl. Artif. Intell. 87, no. March 2019, p. 103312 (2020)

    Google Scholar 

  8. McDonald, A.D., Lee, J.D., Schwarz, C., Brown, T.L.: A contextual and temporal algorithm for driver drowsiness detection. Accid. Anal. Prev. 113(January), 25–37 (2018)

    Article  Google Scholar 

  9. Elamrani Abou Elassad, Z., Mousannif, H.: Understanding driving behavior: measurement, modeling and analysis. Adv. Intell. Syst. Comput. 5 (2019)

    Google Scholar 

  10. Pirdavani, A., et al.: Application of a rule-based approach in real-time crash risk prediction model development using loop detector data. Traffic Inj. Prev. 16(8), 786–791 (2015)

    Article  Google Scholar 

  11. Ba, Y., Zhang, W., Wang, Q., Zhou, R., Ren, C.: Crash prediction with behavioral and physiological features for advanced vehicle collision avoidance system. Transp. Res. Part C Emerg. Technol. 74, 22–33 (2017)

    Article  Google Scholar 

  12. Wang, C., Xu, C., Dai, Y.: A crash prediction method based on bivariate extreme value theory and video-based vehicle trajectory data. Accid. Anal. Prev., 123, no. December 2018, 365–373 (2019)

    Google Scholar 

  13. Lee, D., Derrible, S., Pereira, F.C.: Comparison of four types of artificial neural network and a multinomial logit model for travel mode choice modeling. Transp. Res. Rec. J. Transp. Res. Board 2672(49), 101–112 (2018)

    Article  Google Scholar 

  14. Elamrani Abou Elassad, Z., Mousannif, H., Al Moatassime, H.: A real-time crash prediction fusion framework: an imbalance-aware strategy for collision avoidance systems. Transp. Res. Part C Emerg. Technol. 118, no. November 2019, p. 102708 (2020)

    Google Scholar 

  15. Park, H., Haghani, A., Samuel, S., Knodler, M.A.: Real-time prediction and avoidance of secondary crashes under unexpected traffic congestion. Accid. Anal. Prev. 112, no. May 2017, 39–49 (2018)

    Google Scholar 

  16. Parsa, A.B., Taghipour, H., Derrible, S., Mohammadian, A.: Real-time accident detection: coping with imbalanced data. Accid. Anal. Prev. 129, no. May, 202–210 (2019)

    Google Scholar 

  17. Basu, S., et al.: Deep neural networks for texture classification—a theoretical analysis. Neural Netw. 97, 173–182 (2018)

    Article  MATH  Google Scholar 

  18. Li, Y., Chen, M., Lu, X., Zhao, W.: Research on optimized GA-SVM vehicle speed prediction model based on driver-vehicle-road-traffic system. Sci. CHINA Technol. Sci. 61(5), 782–790 (2018)

    Article  Google Scholar 

  19. Joo, Y.-J., Kho, S.-Y., Kim, D.-K., Park, H.-C.: A data-driven Bayesian network for probabilistic crash risk assessment of individual driver with traffic violation and crash records. Accid. Anal. Prev. 176, 106790 (2022)

    Article  Google Scholar 

  20. Elamrani Abou Elassad, Z., Mousannif, H., Al Moatassime, H.: Class-imbalanced crash prediction based on real-time traffic and weather data: a driving simulator study. Traffic Inj. Prev. 1–8 (2020)

    Google Scholar 

  21. Ameksa, M., Mousannif, H., Moatassime, H.A.L., Elassad, Z.E.A., Behavior, D., Driving, N.: Toward flexible data collection of driving behavior. XLIV, no. October, pp. 7–8 (2020)

    Google Scholar 

  22. Govinda, L., Raju, M.R.S.K., Shankar, K.V.R.R.: Pedestrian-vehicle interaction severity level assessment at uncontrolled intersections using machine learning algorithms. Saf. Sci. 153, 105806 (2022)

    Article  Google Scholar 

  23. Liu, J., Boyle, L.N., Banerjee, A.G.: Predicting interstate motor carrier crash rate level using classification models. Accid. Anal. Prev. 120, no. November 2017, 211–218 (2018)

    Google Scholar 

  24. Zicat, E., Bennett, J.M., Chekaluk, E., Batchelor, J.: Cognitive function and young drivers : the relationship between driving, attitudes, personality and cognition. Transp. Res. Part F Psychol. Behav. 55, 341–352 (2018)

    Article  Google Scholar 

  25. Moral-García, S., Castellano, J.G., Mantas, C.J., Montella, A., Abellán, J.: Decision tree ensemble method for analyzing traffic accidents of novice drivers in urban areas. Entropy 21(4), 360 (2019)

    Article  MathSciNet  Google Scholar 

  26. FHWA. How Do Weather Events Impact Roads? - FHWA Road Weather Management (2016). https://ops.fhwa.dot.gov/weather/q1_roadimpact.htm. Accessed 25 Jul 2019

  27. Elamrani Abou Elassad, Z., Mousannif, H., Al Moatassime, H.: A proactive decision support system for predicting traffic crash events: a critical analysis of imbalanced class distribution. Knowledge-Based Syst. 205, 106314 (2020)

    Google Scholar 

  28. Parsa, A.B., Movahedi, A., Taghipour, H., Derrible, S., Mohammadian, A.: Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis. Accid. Anal. Prev. 136 (2020)

    Google Scholar 

  29. R. Akuh, M. Donani, S. Okyere, and E. K. Gyamfi, “The impact of perceived safety, weather condition and convenience on motorcycle helmet use: The mediating role of traffic law enforcement and road safety education,” IATSS Res., 2023

    Google Scholar 

  30. Malin, F., Norros, I., Innamaa, S.: Accident risk of road and weather conditions on different road types. Accid. Anal. Prev. 122, no. August 2018, 181–188 (2019)

    Google Scholar 

  31. Slightly Mad Studios. Project CARS - THE ULTIMATE DRIVER JOURNEY. https://www.projectcarsgame.com/. Accessed 23 Jun 2019

  32. Al, S., Dener, M.: STL-HDL: a new hybrid network intrusion detection system for imbalanced dataset on big data environment. Comput. Secur. 110, 102435 (2021)

    Article  Google Scholar 

  33. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)

    Article  MATH  Google Scholar 

  34. Kitali, A.E., Alluri, P., Sando, T., Wu, W.: Identification of secondary crash risk factors using penalized logistic regression model. Transp. Res. Rec. (2019)

    Google Scholar 

  35. Gosain, A., Sardana, S.: Farthest SMOTE: a modified SMOTE approach. In: Computational Intelligence in Data Mining, Springer, pp. 309–320 (2019). https://doi.org/10.1007/978-981-10-8055-5_28

  36. Basheer, I., Hajmeer, M.: Artificial neural networks: fundamentals, computing, design, and application. J. Microbiol. Methods 43(1), 3–31 (2000)

    Article  Google Scholar 

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

    Article  Google Scholar 

  38. Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer New York (1995). https://doi.org/10.1007/978-1-4757-3264-1

  39. Beryl Princess, P.J., Silas, S., Rajsingh, E.B.: Classification of road accidents using SVM and KNN. In: Advances in Artificial Intelligence and Data Engineering Springer, pp. 27–41 (2021). https://doi.org/10.1007/978-981-15-3514-7_3

  40. Sirsat, M.S., Fermé, E., Câmara, J.: Machine learning for brain stroke: a review. J. Stroke Cerebrovasc. Dis. 29(10), 105162 (2020)

    Article  Google Scholar 

  41. Ramedani, Z., Omid, M., Keyhani, A., Shamshirband, S., Khoshnevisan, B.: Potential of radial basis function based support vector regression for global solar radiation prediction. Renew. Sustain. Energy Rev. 39, 1005–1011 (2014)

    Article  Google Scholar 

  42. Murphy, K.P.: Dynamic Bayesian Networks: Representation, Inference and Learning. University of California, Berkeley (2002)

    Google Scholar 

  43. Kohavi, R., Kohavi, R.: A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection, pp. 1137--1143 (1995)

    Google Scholar 

  44. West, D.: Neural network credit scoring models. Comput. Oper. Res. 27(11–12), 1131–1152 (2000)

    Article  MATH  Google Scholar 

Download references

Acknowledgment

This research was jointly supported by (1) the Moroccan Ministry of Equipment, Transport and Logistics, and (2) the Moroccan National Center for Scientific and Technical Research (CNRST).

Author information

Authors and Affiliations

  1. SARS Research Team, ENSAS, Cadi Ayyad University, Safi, Morocco

    Zouhair Elamrani Abou Elassad & Dauha Elamrani Abou Elassad

  2. LISI Laboratory, FSSM, Cadi Ayyad University, Marrakesh, Morocco

    Mohamed Ameksa & Hajar Mousannif

Authors
  1. Zouhair Elamrani Abou Elassad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohamed Ameksa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dauha Elamrani Abou Elassad
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hajar Mousannif
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zouhair Elamrani Abou Elassad .

Editor information

Editors and Affiliations

  1. Sultan Moulay Slimane University, Beni-Mellal, Morocco

    Rachid El Ayachi

  2. Sultan Moulay Slimane University, Beni-Mellal, Morocco

    Mohamed Fakir

  3. Sultan Moulay Slimane University, Beni-Mellal, Morocco

    Mohamed Baslam

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Elamrani Abou Elassad, Z., Ameksa, M., Elamrani Abou Elassad, D., Mousannif, H. (2023). Machine Learning Prediction of Weather-Induced Road Crash Events for Experienced and Novice Drivers: Insights from a Driving Simulator Study. In: El Ayachi, R., Fakir, M., Baslam, M. (eds) Business Intelligence. CBI 2023. Lecture Notes in Business Information Processing, vol 484 . Springer, Cham. https://doi.org/10.1007/978-3-031-37872-0_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-37872-0_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-37871-3

  • Online ISBN: 978-3-031-37872-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation