Probabilistic versus Non-Probabilistic Machine Learning Approaches for Estimating the Severity of Crashes in Construction Work Zones
Publication: Construction Research Congress 2024
ABSTRACT
Roadway work zones often present hazardous conditions for drivers, pedestrians, and construction workers. Understanding the factors contributing to work zone crashes and their severity can assist the departments of transportation (DOTs) in safety management and planning. Accordingly, this study investigated the effect of freeway work zones on the severity of crashes by employing machine learning algorithms. The model was developed using crash data and construction zone information from Utah between 2017 and 2021. This study compares the performance of probabilistic and non-probabilistic machine learning models in predicting work zone crash severity using the KABCO severity scale. All three models achieved promising accuracy levels, with Extreme Gradient Boosting (XGB) achieving the highest accuracy at 86%, followed by Gaussian Naïve Bayes (GNB) with an accuracy of 76%, and complement Naïve Bayes (CNB) with an accuracy of 74%. The findings of this study offer valuable insights for the safety management plans of DOTs.
Get full access to this article
View all available purchase options and get full access to this chapter.
REFERENCES
Chen, M.-M., and Chen, M.-C. (2020). Modeling road accident severity with comparisons of logistic regression, decision tree and random forest. Information, 11(5), 270.
Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., and Zhou, T. (2015). Xgboost: extreme gradient boosting. R Package Version 0.4-2, 1(4), 1–4.
Cheng, Y., Wu, K., Li, H., Parker, S., Ran, B., and Noyce, D. (2022). Work zone crash occurrence prediction based on planning stage work zone configurations using an artificial neural network. Transportation Research Record, 2676(11), 377–384.
Dimitrijevic, B., Khales, S. D., Asadi, R., and Lee, J. (2022). Short-term segment-level crash risk prediction using advanced data modeling with proactive and reactive crash data. Applied Sciences, 12(2), 856.
Hosseinzadeh, A., Moeinaddini, A., and Ghasemzadeh, A. (2021). Investigating factors affecting severity of large truck-involved crashes: Comparison of the SVM and random parameter logit model. Journal of Safety Research, 77, 151–160.
Mashhadi, A. H., Farhadmanesh, M., Rashidi, A., and Marković, N. (2021a). Review of methods for estimating construction work zone capacity. Transportation Research Record, 2675(9), 382–397.
Mashhadi, A. H., Farhadmanesh, M., Rashidi, A., and Marković, N. (2021b). State-of-the-Art Methods in Estimating Freeway Work zones Capacity: A Literature Review. Transportation Research Board 100th Annual MeetingTransportation Research Board, TRBAM-21-01863.
Mohammadi, P., Rashidi, A., Malekzadeh, M., and Tiwari, S. (2023). Evaluating various machine learning algorithms for automated inspection of culverts. Engineering Analysis with Boundary Elements, 148, 366–375.
National Work Zone Safety (2022). (n.d.). Retrieved July 26, 2023, from https://workzonesafety.org/work-zone-data/.
Pai, V., and Adesh, N. D. (2021). Comparative analysis of machine learning algorithms for intrusion detection. IOP Conference Series: Materials Science and Engineering, 1013(1), 012038.
Panicker, A. K., and Ramadurai, G. (2022). Injury severity prediction model for two-wheeler crashes at mid-block road sections. International Journal of Crashworthiness, 27(2), 328–336.
Sabillon, C., Rashidi, A., Samanta, B., Davenport, M. A., and Anderson, D. V. (2020). Audio-based bayesian model for productivity estimation of cyclic construction activities. Journal of Computing in Civil Engineering, 34(1), 04019048.
Santos, B., Trindade, V., Polónia, C., and Picado-Santos, L. (2021). Detecting risk factors of road work zone crashes from the information provided in police crash reports: the case study of Portugal. Safety, 7(1), 12.
Seref, B., and Bostanci, E. (2019). Performance comparison of Naïve Bayes and complement Naïve Bayes algorithms. 2019 6th International Conference on Electrical and Electronics Engineering (ICEEE), 131–138.
Sze, N. N., and Song, Z. (2019). Factors contributing to injury severity in work zone related crashes in New Zealand. International Journal of Sustainable Transportation, 13(2), 148–154.
Zhang, K., and Hassan, M. (2019a). Identifying the factors contributing to injury severity in work zone rear-end crashes. Journal of Advanced Transportation, 2019.
Zhang, K., and Hassan, M. (2019b). Injury severity analysis of nighttime work zone crashes. 2019 5th International Conference on Transportation Information and Safety (ICTIS), 1301–1308.
Information & Authors
Information
Published In
Construction Research Congress 2024
Pages: 445 - 454
History
Published online: Mar 18, 2024
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Business management
- Computer programming
- Computing in civil engineering
- Construction engineering
- Construction industry
- Construction management
- Construction sites
- Employment
- Engineering fundamentals
- Infrastructure
- Infrastructure construction
- Labor
- Mathematics
- Model accuracy
- Models (by type)
- Personnel management
- Practice and Profession
- Probability
- Traffic accidents
- Traffic engineering
- Traffic management
- Transportation engineering
- Work zones
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.