265
Views
3
CrossRef citations to date
0
Altmetric
Research Articles

A hybrid clustering and random forest model to analyse vulnerable road user to motor vehicle (VRU-MV) crashes

, , , , , & show all
Pages 338-351 | Received 31 Aug 2022, Accepted 11 Feb 2023, Published online: 22 Feb 2023

References

  • Behnood, A., & Mannering, F. (2017). Determinants of bicyclist injury severities in bicycle-vehicle crashes: A random parameters approach with heterogeneity in means and variances. Analytic Methods in Accident Research, 16, 35–47. https://doi.org/10.1016/j.amar.2017.08.001
  • Blazquez, C. A., & Celis, M. S. (2013). A spatial and temporal analysis of child pedestrian crashes in Santiago, Chile. Accident; Analysis and Prevention, 50, 304–311.https://doi.org/10.1016/j.aap.2012.05.001
  • Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
  • Bujosa, A., Riera, A., & Hicks, R. L. (2010). Combining discrete and continuous representations of preference heterogeneity: A latent class approach. Environmental and Resource Economics, 47(4), 477–493. https://doi.org/10.1007/s10640-010-9389-y
  • Cai, Q., Abde-Aty, M., & Castro, S. (2021). Explore effects of bicycle facilities and exposure on bicycle safety at intersections. International Journal of Sustainable Transportation, 15(8), 592–603. https://doi.org/10.1080/15568318.2020.1772415
  • Chang, F., Yasmin, S., Huang, H., Chan, A. H. S., & Haque, M. M. (2021). Injury severity analysis of motorcycle crashes: A comparison of latent class clustering and latent segmentation based models with unobserved heterogeneity. Analytic Methods in Accident Research, 32, 100188. https://doi.org/10.1016/j.amar.2021.100188
  • Cheng, W., Gill, G. S., Sakrani, T., Dasu, M., & Zhou, J. (2017). Predicting motorcycle crash injury severity using weather data and alternative Bayesian multivariate crash frequency models. Accident; Analysis and Prevention, 108, 172–180.https://doi.org/10.1016/j.aap.2017.08.032
  • Chen, P., & Shen, Q. (2016). Built environment effects on cyclist injury severity in automobile-involved bicycle crashes. Accident; Analysis and Prevention, 86, 239–246.https://doi.org/10.1016/j.aap.2015.11.002
  • Chen, Y., Luo, R., Yang, H., King, M., & Shi, Q. (2020). Applying latent class analysis to investigate rural highway single-vehicle fatal crashes in China. Accident; Analysis and Prevention, 148, 105840. https://doi.org/10.1016/j.aap.2020.10584
  • Decker, S., Otte, D., Cruz, D. L., Müller, C. W., Omar, M., Krettek, C., & Brand, S. (2016). Injury severity of pedestrians, bicyclists and motorcyclists resulting from crashes with reversing cars. Accident; Analysis and Prevention, 94, 46–51.https://doi.org/10.1016/j.aap.2016.05.010
  • De Oña, J., López, G., Mujalli, R., & Calvo, F. J. (2013). Analysis of traffic accidents on rural highways using Latent Class Clustering and Bayesian Networks. Accident; Analysis and Prevention, 51, 1–10.https://doi.org/10.1016/j.aap.2012.10.016
  • Dong, C., Khattak, A. J., Shao, C., & Xie, K. (2019). Exploring the factors contribute to the injury severities of vulnerable roadway user involved crashes. International Journal of Injury Control and Safety Promotion, 26(3), 302–314.https://doi.org/10.1080/17457300.2019.1595665
  • Feng, S., Li, Z., Ci, Y., & Zhang, G. (2016). Risk factors affecting fatal bus accident severity: Their impact on different types of bus drivers. Accident; Analysis and Prevention, 86, 29–39.https://doi.org/10.1016/j.aap.2015.09.025
  • Ghomi, H., & Hussein, M. (2021). An integrated clustering and copula-based model to assess the impact of intersection characteristics on violation-related collisions. Accident; Analysis and Prevention, 159, 106283. https://doi.org/10.1016/j.aap.2021.10628
  • Hertach, P., Uhr, A., Niemann, S., & Cavegn, M. (2018). Characteristics of single-vehicle crashes with e-bikes in Switzerland. Accident; Analysis and Prevention, 117, 232–238.https://doi.org/10.1016/j.aap.2018.04.021
  • Hosseinpour, M., Madsen, T. K. O., Olesen, A. V., & Lahrmann, H. (2021). An in-depth analysis of self-reported cycling injuries in single and multiparty bicycle crashes in Denmark. Journal of Safety Research, 77, 114–124.https://doi.org/10.1016/j.jsr.2021.02.009
  • Iranitalab, A., & Khattak, A. (2017). Comparison of four statistical and machine learning methods for crash severity prediction. Accident; Analysis and Prevention, 108, 27–36.https://doi.org/10.1016/j.aap.2017.08.008
  • Islam, S., & Mannering, F. (2006). Driver aging and its effect on male and female single-vehicle accident injuries: Some additional evidence. Journal of Safety Research, 37(3), 267–276.https://doi.org/10.1016/j.jsr.2006.04.003
  • Kang, Y., & Khattak, A. (2017). Cluster-based approach to analyzing crash injury severity at highway-rail grade crossings. Transportation Research Record: Journal of the Transportation Research Board, 2608(1), 58–69. https://doi.org/10.3141/2608-07
  • Kamel, M. B., Sayed, T., & Osama, A. (2019). Accounting for mediation in cyclist-vehicle crash models: A Bayesian mediation analysis approach. Accident; Analysis and Prevention, 131, 122–130.https://doi.org/10.1016/j.aap.2019.06.009
  • Lanza, S. T., & Rhoades, B. L. (2013). Latent class analysis: An alternative perspective on subgroup analysis in prevention and treatment. Prevention Science: The Official Journal of the Society for Prevention Research, 14(2), 157–168.https://doi.org/10.1007/s11121-011-0201-1
  • Li, J., Fang, S., Guo, J., Fu, T., & Qiu, M. (2021). A motorcyclist-injury severity analysis: A comparison of single-, two-, and multi-vehicle crashes using latent class ordered probit model. Accident; Analysis and Prevention, 151, 105953.https://doi.org/10.1016/j.aap.2020.105953
  • Li, Y., Song, L., & Fan, W. (2021). Day-of-the-week variations and temporal instability of factors influencing pedestrian injury severity in pedestrian-vehicle crashes: A random parameters logit approach with heterogeneity in means and variances. Analytic Methods in Accident Research, 29, 100152. https://doi.org/10.1016/j.amar.2020.100152
  • Li, Y., & Fan, W. D. (2019). Modelling severity of pedestrian-injury in pedestrian-vehicle crashes with latent class clustering and partial proportional odds model: A case study of North Carolina. Accident; Analysis and Prevention, 131, 284–296.https://doi.org/10.1016/j.aap.2019.07.008
  • Li, Z., Chen, C., Ci, Y., Zhang, G., Wu, Q., Liu, C., & Qian, Z. (2018). Examining driver injury severity in intersection-related crashes using cluster analysis and hierarchical Bayesian models. Accident; Analysis and Prevention, 120, 139–151.https://doi.org/10.1016/j.aap.2018.08.009
  • Li, Z., Wu, Q., Ci, Y., Chen, C., Chen, X., & Zhang, G. (2019). Using latent class analysis and mixed logit model to explore risk factors on driver injury severity in single-vehicle crashes. Accident; Analysis and Prevention, 129, 230–240.https://doi.org/10.1016/j.aap.2019.04.001
  • Liu, P., & Fan, W. (2020). Exploring injury severity in head-on crashes using latent class clustering analysis and mixed logit model: A case study of North Carolina. Accident; Analysis and Prevention, 135, 105388.https://doi.org/10.1016/j.aap.2019.105388
  • Liu, J., Khattak, A. J., Li, X., Nie, Q., & Ling, Z. (2020). Bicyclist injury severity in traffic crashes: A spatial approach for geo-referenced crash data to uncover nonstationary correlates. Journal of Safety Research, 73, 25–35.https://doi.org/10.1016/j.jsr.2020.02.006
  • Liu, Z., & Fan, W. D. (2021). Exploring bicyclist injury severity in bicycle-vehicle crashes using latent class clustering analysis and partial proportional odds models. Journal of Safety Research, 76, 101–117. https://doi.org/10.1016/j.jsr.2020.11.012
  • Linzer, D. A., & Lewis, J. B. (2011). poLCA: An R package for polytomous variable latent class analysis. Journal of Statistical Software, 42(10), 1–92. https://doi.org/10.18637/jss.v042.i10
  • Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing System, 30, 4765–4774. http://refhub.elsevier.com/S0013-9351(21)00954-3/sref30
  • Ma, Z., Mei, G., & Cuomo, S. (2021). An analytic framework using deep learning for prediction of traffic accident injury severity based on contributing factors. Accident; Analysis and Prevention, 160, 106322.https://doi.org/10.1016/j.aap.2021.106322
  • Mannering, F., Shankar, V., & Bhat, C. R. (2016). Unobserved heterogeneity and the statistical analysis of highway accident data. Analytic Methods in Accident Research, 11, 1–16. https://doi.org/10.1016/j.amar.2016.04.001
  • Mahmoud, N., Abdel-Aty, M., Cai, Q., & Zheng, Q. (2021). Vulnerable road users’ crash hotspot identification on multi-lane arterial roads using estimated exposure and considering context classification. Accident; Analysis and Prevention, 159, 106294.https://doi.org/10.1016/j.aap.2021.106294
  • Mayhew, D. R., Simpson, H. M., & Pak, A. (2003). Changes in collision rates among novice drivers during the first months of driving. Accident; Analysis and Prevention, 35(5), 683–691.https://doi.org/10.1016/s0001-4575(02)00047-7
  • Mohamed, G. M., Saunier, N., Miranda-Moreno, L. F., & Ukkusuri, S. V. (2013). A clustering regression approach: A comprehensive injury severity analysis of pedestrian-vehicle crashes in New York, US and Montreal, Canada. Safety Science, 54, 27–37.
  • Moore, D. N., Schneider, W. H., Savolainen, P. T., & Farzaneh, M. (2011). Mixed logit analysis of bicyclist injury severity resulting from motor vehicle crashes at intersection and non-intersection locations. Accident; Analysis and Prevention, 43(3), 621–630.https://doi.org/10.1016/j.aap.2010.09.015
  • Morgan, A., & Mannering, F. (2011). The effects of road-surface conditions, age, and gender on driver-injury severities. Accident; Analysis and Prevention, 43(5), 1852–1863.https://doi.org/10.1016/j.aap.2011.04.024
  • Munira, S., Sener, IN., & Dai, B. (2020). A Bayesian spatial Poisson-lognormal model to examine pedestrian crash severity at signalized intersections. Accident; Analysis and Prevention, 144, 105679.https://doi.org/10.1016/j.aap.2020.105679
  • Mueller, B. A., Rivara, F. P., & Bergman, A. B. (1988). Urban-rural location and the risk of dying in a pedestrian-vehicle collision. Journal of Trauma-Injury Infection and Critical Care, 28, 91–94.
  • Rafiq, R., & McNally, M. G. (2021). Heterogeneity in activity-travel patterns of public transit users: An application of latent class analysis. Transportation Research Part A: Policy and Practice, 152, 1–18. https://doi.org/10.1016/j.tra.2021.07.011
  • Raihan, M. A., Hossain, M., & Hasan, T. (2018). Data mining in road crash analysis: The context of developing countries. International Journal of Injury Control and Safety Promotion, 25(1), 41–52.https://doi.org/10.1080/17457300.2017.1323929
  • Samerei, S. A., Aghabayk, K., Shiwakoti, N., & Mohammadi, A. (2021). Using latent class clustering and binary logistic regression to model Australian cyclist injury severity in motor vehicle-bicycle crashes. Journal of Safety Research, 79, 246–256.https://doi.org/10.1016/j.jsr.2021.09.005
  • Sasidharan, L., Wu, K. F., & Menendez, M. (2015). Exploring the application of latent class cluster analysis for investigating pedestrian crash injury severities in Switzerland. Accident; Analysis and Prevention, 85, 219–228.https://doi.org/10.1016/j.aap.2015.09.020
  • Se, C., Champahom, T., Jomnonkwao, S., Chaimuang, P., & Ratanavaraha, V. (2021). Empirical comparison of the effects of urban and rural crashes on motorcyclist injury severities: A correlated random parameters ordered probit approach with heterogeneity in means. Accident; Analysis and Prevention, 161, 106352.https://doi.org/10.1016/j.aap.2021.106352
  • Song, L., Fan, W. D., Li, Y., & Wu, P. (2021). Exploring pedestrian injury severities at pedestrian-vehicle crash hotspots with an annual upward trend: A spatiotemporal analysis with latent class random parameter approach. Journal of Safety Research, 76, 184–196.https://doi.org/10.1016/j.jsr.2020.12.008
  • Stipancic, J., Zangenehpour, S., Miranda-Moreno, L., Saunier, N., & Granie, M. A. (2016). Investigating the gender differences on bicycle-vehicle conflicts at urban intersections using an ordered logit methodology. Accident; Analysis and Prevention, 97, 19–27.https://doi.org/10.1016/j.aap.2016.07.033
  • Sun, M., Sun, X., & Shan, D. (2019). Pedestrian crash analysis with latent class clustering method. Accident; Analysis and Prevention, 124, 50–57.https://doi.org/10.1016/j.aap.2018.12.016
  • Sun, Z., Xing, Y., Wang, J., Gu, X., Lu, H., & Chen, Y. (2022). Exploring injury severity of vulnerable road user involved crashes across seasons: A hybrid method integrating random parameter logit model and Bayesian network. Safety Science, 150, 105682. https://doi.org/10.1016/j.ssci.2022.105682
  • Sun, Z., Xing, Y., Gu, X., & Chen, Y. (2022). Influence factors on injury severity of bicycle-motor vehicle crashes: A two-stage comparative analysis of urban and suburban areas in Beijing. Traffic Injury Prevention, 23(2), 118–124.https://doi.org/10.1080/15389588.2021.2024523
  • Tang, J., Liang, J., Han, C., Li, Z., & Huang, H. (2019). Crash injury severity analysis using a two-layer Stacking framework. Accident; Analysis and Prevention, 122, 226–238.https://doi.org/10.1016/j.aap.2018.10.016
  • Train, K. E. (2009). Discrete choice methods with simulation. Cambridge University Press.
  • Ulfarsson, G., & Mannering, F. (2004). Differences in male and female injury severities in sport-utility vehicle, minivan, pickup and passenger car accidents. Accident; Analysis and Prevention, 36(2), 135–147. https://doi.org/10.1016/s0001-4575(02)00135-5
  • Wen, X., Xie, Y., Wu, L., & Jiang, L. (2021). Quantifying and comparing the effects of key risk factors on various types of roadway segment crashes with LightGBM and SHAP. Accident; Analysis and Prevention, 159, 106261. https://doi.org/10.1016/j.aap.2021.106261
  • World Health Organization. (2020). World health statistics: Monitoring health for the SDGs sustainable development goals. World Health Organization.
  • Xiao, D., Saric, Z., Xu, X., & Yuan, Q. (2023). Investigating injury severity of pedestrian-vehicle crashes by integrating latent class cluster analysis and unbalanced panel mixed ordered probit model. Journal of Transportation Safety & Security, 15(2), 83–102. https://doi.org/10.1080/19439962.2022.2033900
  • Yang, C., Chen, M. Y., & Yuan, Q. (2021). The application of XGBoost and SHAP to examining the factors in freight truck-related crashes: An exploratory analysis. Accident; Analysis and Prevention, 158, 106153. https://doi.org/10.1016/j.aap.2021.106153
  • Yoo, J., & Ready, R. C. (2014). Preference heterogeneity for renewable energy technology. Energy Economics, 42, 101–114. https://doi.org/10.1016/j.eneco.2013.12.007
  • Yuan, C., Li, Y., Huang, H., Wang, S., Sun, Z., & Wang, H. (2022). Application of explainable machine learning for real-time safety analysis toward a connected vehicle environment. Accident; Analysis and Prevention, 171, 106681.https://doi.org/10.1016/j.aap.2022.106681
  • Yu, R., & Abdel-Aty, M. (2014). Analyzing crash injury severity for a mountainous freeway incorporating real-time traffic and weather data. Safety Science, 63, 50–56. https://doi.org/10.1016/j.ssci.2013.10.012

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.