Integration of machine learning and statistical models for crash frequency modeling

References

• Aguero-Valverde, J., and P. P. Jovanis. 2008. “Analysis of Road Crash Frequency with Spatial Models.” Transportation Research Record: Journal of the Transportation Research Board 2061 (1): 55–63. doi:10.3141/2061-07.
   Web of Science ®Google Scholar
• Albin, R., V. Brinkly, J. Cheung, F. Julian, C. Satterfield, W. J. Stein, and F. R. Hanscom. 2016. Low-cost treatments for horizontal curve safety 2016 (No. FHWA-SA-15-084).” https://rosap.ntl.bts.gov/view/dot/50421
   Google Scholar
• Altmann, A., L. Toloşi, O. Sander, and T. Lengauer. 2010. “Permutation Importance: A Corrected Feature Importance Measure.” Bioinformatics 26 (10): 1340–1347. doi:10.1093/bioinformatics/btq134.
   PubMed Web of Science ®Google Scholar
• Anastasopoulos, P. C., and F. L. Mannering. 2009. “A Note on Modeling Vehicle Accident Frequencies with random-parameters Count Models.” Accident Analysis and Prevention 41 (1): 153–159. doi:10.1016/j.aap.2008.10.005.
   PubMed Web of Science ®Google Scholar
• Browne, M. W. 2000. “Cross-Validation Methods.” Journal of Mathematical Psychology 44 (1): 108–132. doi:10.1006/JMPS.1999.1279.
   PubMed Web of Science ®Google Scholar
• Butsick, A. J., J. S. Wood, and P. P. Jovanis. 2017. “Using Network Screening Methods to Determine Locations with Specific Safety Issues: A Design Consistency Case Study.” Accident Analysis & Prevention 106: 223–233. doi:10.1016/J.AAP.2017.06.006.
   PubMed Web of Science ®Google Scholar
• Cameron, A. C., and P. K. Trivedi. 2013. Regression Analysis of Count Data. Vol. 53. Cambridge University Press. doi:10.1017/CBO9781139013567.
   Google Scholar
• Chang, L. Y. 2005. “Analysis of Freeway Accident Frequencies: Negative Binomial Regression versus Artificial Neural Network.” Safety Science 43 (8): 541–557. doi:10.1016/j.ssci.2005.04.004.
   Web of Science ®Google Scholar
• Chang, L. Y., and W. C. Chen. 2005. “Data Mining of tree-based Models to Analyze Freeway Accident Frequency.” Journal of Safety Research 36 (4): 365–375. doi:10.1016/j.jsr.2005.06.013.
   PubMed Web of Science ®Google Scholar
• Chang, L. Y., and H. W. Wang. 2006. “Analysis of Traffic Injury Severity: An Application of non-parametric Classification Tree Techniques.” Accident Analysis and Prevention 38 (5): 1019–1027. doi:10.1016/j.aap.2006.04.009.
   PubMed Web of Science ®Google Scholar
• Cheng, L., S. R. Geedipally, and D. Lord. 2013. “The Poisson-Weibull Generalized Linear Model for Analyzing Motor Vehicle Crash Data.” Safety Science 54: 38–42. doi:10.1016/j.ssci.2012.11.002.
   Web of Science ®Google Scholar
• Chen, T., and C. Guestrin (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13-17 August 2016, 785–794. 10.1145/2939672.2939785
   Google Scholar
• De Oña, J., G. López, and J. Abellán. 2013. “Extracting Decision Rules from Police Accident Reports through Decision Trees.” Accident Analysis and Prevention 50: 1151–1160. doi:10.1016/j.aap.2012.09.006.
   PubMed Web of Science ®Google Scholar
• Donnell, E., R. Porter, L. Li, I. Hamilton, S. Himes, and J. Wood. 2019. “Reducing Roadway Departure Crashes at Horizontal Curve Sections on Two-Lane Rural Highways (No. FHWA-SA-19-005).” https://rosap.ntl.bts.gov/view/dot/55604
   Google Scholar
• Doshi-Velez, F., and B. Kim. 2017. Towards A Rigorous Science of Interpretable Machine Learning. http://arxiv.org/abs/1702.08608.
   Google Scholar
• Du, M., N. Liu, and X. Hu. 2018. “Techniques for Interpretable Machine Learning.” ArXiv. http://arxiv.org/abs/1808.00033.
   Google Scholar
• Dziugaite, G. K., S. Ben-David, and D. M. Roy. 2020. “Enforcing Interpretability and Its Statistical Impacts: Trade-offs between Accuracy and Interpretability.” https://arxiv.org/abs/2010.13764v2
   Google Scholar
• El-Basyouny, K., and T. Sayed. 2009. “Accident Prediction Models with Random Corridor Parameters.” Accident Analysis and Prevention 41 (5): 1118–1123. doi:10.1016/j.aap.2009.06.025.
   PubMed Web of Science ®Google Scholar
• Federal Highway Administration (FHWA). 2022. “Highway Safety Information System (HSIS).” https://www.hsisinfo.org
   Google Scholar
• Friedman, J. H. 2001. “Greedy Function Approximation: A Gradient Boosting Machine.” Annals of Statistics 29 (5): 1189–1232. doi:10.1214/aos/1013203451.
   Web of Science ®Google Scholar
• Friedman, J. H., and B. E. Popescu. 2008. “Predictive Learning via Rule Ensembles.” Annals of Applied Statistics 2 (3): 916–954. doi:10.1214/07-AOAS148.
   Web of Science ®Google Scholar
• Gooch, J. P., V. V. Gayah, and E. T. Donnell. 2016. “Quantifying the Safety Effects of Horizontal Curves on two-way, two-lane Rural Roads.” Accident Analysis & Prevention 92: 71–81. doi:10.1016/J.AAP.2016.03.024.
   PubMed Web of Science ®Google Scholar
• Grant, R. J., M. A. Gregor, P. W. Beck, and R. F. Maio. 2000. “A Comparison of Data Sources for Motor Vehicle Crash Characteristic Accuracy.” Academic Emergency Medicine 7 (8): 892–897. doi:10.1111/J.1553-2712.2000.TB02067.X.
   PubMed Web of Science ®Google Scholar
• Hadi, M. A., J. Aruldhas, L. F. Chow, and J. A. Wattleworth. 1995. “Estimating Safety Effects of cross-section Design for Various Highway Types Using Negative Binomial Regression.” Transportation Research Record 1500: 169–177.
   Google Scholar
• Hancox-Li, L. (2020). Robustness in Machine Learning Explanations: Does It Matter? Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 640–647. 10.1145/3351095.3372836
   Google Scholar
• Hauer, E. 2015. The Art of Regression Modeling in Road Safety. Springer International Publishing. doi:10.1007/978-3-319-12529-9.
   Google Scholar
• Hilbe, J. M. 2011. Negative Binomial Regression. Cambridge University Press. doi:10.1017/CBO9780511973420.
   Google Scholar
• Hua, Z., and B. Zhang. 2006. “A Hybrid Support Vector Machines and Logistic Regression Approach for Forecasting Intermittent Demand of Spare Parts.” Applied Mathematics and Computation 181 (2): 1035–1048. doi:10.1016/j.amc.2006.01.064.
   Web of Science ®Google Scholar
• Johansson, P. 1996. “Speed Limitation and Motorway Casualties: A Time Series Count Data Regression Approach.” Accident Analysis and Prevention 28 (1): 73–87. doi:10.1016/0001-4575(95)00043-7.
   PubMed Web of Science ®Google Scholar
• Jovanis, P. P., and H. L. Chang. 1986. “Modeling the Relationship of Accidents to Miles Traveled.” Transportation Research Record 42–51.
   Google Scholar
• Kashani, A. T., and A. S. Mohaymany. 2011. “Analysis of the Traffic Injury Severity on two-lane, two-way Rural Roads Based on Classification Tree Models.” Safety Science 49 (10): 1314–1320. doi:10.1016/j.ssci.2011.04.019.
   Web of Science ®Google Scholar
• Kuhn, M., and K. Johnson. 2013. Applied Predictive Modeling. Vol. 26. New York: Springer. doi:10.1007/978-1-4614-6849-3.
   Google Scholar
• Lee, J., and F. Mannering. 2002. “Impact of Roadside Features on the Frequency and Severity of run-off-roadway Accidents: An Empirical Analysis.” Accident Analysis and Prevention 34 (2): 149–161. doi:10.1016/S0001-4575(01)00009-4.
   PubMed Web of Science ®Google Scholar
• Li, X., D. Lord, Y. Zhang, and Y. Xie. 2008. “Predicting Motor Vehicle Crashes Using Support Vector Machine Models.” Accident Analysis and Prevention 40 (4): 1611–1618. doi:10.1016/j.aap.2008.04.010.
   PubMed Web of Science ®Google Scholar
• Lord, D., S. R. Geedipally, and S. D. Guikema. 2010. “Extension of the Application of Conway-Maxwell-Poisson Models: Analyzing Traffic Crash Data Exhibiting Underdispersion.” Risk Analysis 30 (8): 1268–1276. doi:10.1111/j.1539-6924.2010.01417.x.
   PubMed Web of Science ®Google Scholar
• Lord, D., S. D. Guikema, and S. R. Geedipally. 2008. “Application of the Conway-Maxwell-Poisson Generalized Linear Model for Analyzing Motor Vehicle Crashes.” Accident Analysis and Prevention 40 (3): 1123–1134. doi:10.1016/j.aap.2007.12.003.
   PubMed Web of Science ®Google Scholar
• Lord, D., and L. F. Miranda-Moreno. 2008. “Effects of Low Sample Mean Values and Small Sample Size on the Estimation of the Fixed Dispersion Parameter of Poisson-gamma Models for Modeling Motor Vehicle Crashes: A Bayesian Perspective.” Safety Science 46 (5): 751–770. doi:10.1016/j.ssci.2007.03.005.
   Web of Science ®Google Scholar
• Lord, D., and B. J. Park. 2012. Negative binomial regression models and estimation methods, Probability Density and Likelihood Functions. Texas A&M University, Korea Transport Institute, pp. 1-15
   Google Scholar
• Lundberg, S. M., P. G. Allen, and S.-I. Lee. 2017. “A Unified Approach to Interpreting Model Predictions.” Advances in Neural Information Processing Systems 30. https://github.com/slundberg/shap.
   Google Scholar
• Ma, L., X. Yan, and J. Weng. 2015. “Modeling Traffic Crash Rates of Road Segments through a Lognormal Hurdle Framework with Flexible Scale Parameter.” Journal of Advanced Transportation 49 (8): 928–940. doi:10.1002/ATR.1322.
   Web of Science ®Google Scholar
• Miaou, S. P. 1994. “The Relationship between Truck Accidents and Geometric Design of Road Sections: Poisson versus Negative Binomial Regressions.” Accident Analysis and Prevention 26 (4): 471–482. doi:10.1016/0001-4575(94)90038-8.
   PubMed Web of Science ®Google Scholar
• Milton, J., and F. Mannering. 1998. “The Relationship among Highway Geometrics, traffic-related Elements and motor-vehicle Accident Frequencies.” Transportation 25 (4): 395–413. doi:10.1023/a:1005095725001.
   Web of Science ®Google Scholar
• Molnar, C. 2020. Interpretable Machine Learning. https://christophm.github.io/interpretable-ml-book/
   Google Scholar
• Murdoch, W. J., C. Singh, K. Kumbier, R. Abbasi-Asl, and B. Yu. 2019. “Definitions, Methods, and Applications in Interpretable Machine Learning.” Proceedings of the National Academy of Sciences of the United States of America 116 (44): 22071–22080. doi:10.1073/pnas.1900654116.
   PubMed Web of Science ®Google Scholar
• NHTSA. 2020. “National Center for Statistics and Analysis Motor Vehicle Traffic Crash Data Resource Page.” https://crashstats.nhtsa.dot.gov/#!/PublicationList/38
   Google Scholar
• Otte, C. 2013 Safe and interpretable machine learning: A methodological review. Computational intelligence in intelligent data analysis 111–122.
   Google Scholar
• Picard, R. R., and R. D. Cook. 1984. “Cross-validation of Regression Models.” Journal of the American Statistical Association 79 (387): 575–583. doi:10.1080/01621459.1984.10478083.
   Web of Science ®Google Scholar
• Poch, M., and F. Mannering. 1996. “Negative Binomial Analysis of intersection-accident Frequencies.” Journal of Transportation Engineering 122 (2): 105–113. doi:10.1061/(ASCE)0733-947X(1996)122:2(105).
   Web of Science ®Google Scholar
• Rudin, C. 2019. Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead. Nature Machine Intelligence. Vol. 1, 206–215. doi:10.1038/s42256-019-0048-x.
   PubMedGoogle Scholar
• Sellers, K. F., and G. Shmueli. 2010. “A Flexible Regression Model for Count Data.” Annals of Applied Statistics 4 (2): 943–961. doi:10.1214/09-AOAS306.
   Web of Science ®Google Scholar
• Shankar, V. N., R. B. Albin, J. C. Milton, and F. L. Mannering. 1998. “Evaluating Median Crossover Likelihoods with Clustered Accident Counts: An Empirical Inquiry Using the Random Effects Negative Binomial Model.” Transportation Research Record: Journal of the Transportation Research Board 1635 (1): 44–48. doi:10.3141/1635-06.
   Google Scholar
• Shankar, V., F. Mannering, and W. Barfield. 1995. “Effect of Roadway Geometrics and Environmental Factors on Rural Freeway Accident Frequencies.” Accident Analysis and Prevention 27 (3): 371–389. doi:10.1016/0001-4575(94)00078-Z.
   PubMed Web of Science ®Google Scholar
• Shankar, V., J. Milton, and F. Mannering. 1997. “Modeling Accident Frequencies as zero-altered Probability Processes: An Empirical Inquiry.” Accident Analysis and Prevention 29 (6): 829–837. doi:10.1016/S0001-4575(97)00052-3.
   PubMed Web of Science ®Google Scholar
• Shinar, D., J. R. Treat, and S. T. McDonald. 1983. “The Validity of Police Reported Accident Data.” Accident Analysis & Prevention 15 (3): 175–191. doi:10.1016/0001-4575(83)90018-0.
   Web of Science ®Google Scholar
• Shmueli, G. 2010. “To Explain or to Predict?” Statistical Science 25 (3): 289–310. doi:10.1214/10-STS330.
   Web of Science ®Google Scholar
• Siddiqui, C., M. Abdel-Aty, and H. Huang. 2012. “Aggregate Nonparametric Safety Analysis of Traffic Zones.” Accident Analysis and Prevention 45: 317–325. doi:10.1016/j.aap.2011.07.019.
   PubMed Web of Science ®Google Scholar
• Sun, J., and J. Sun. 2016. “Real-time Crash Prediction on Urban Expressways: Identification of Key Variables and a Hybrid Support Vector Machine Model.” IET Intelligent Transport Systems 10 (5): 331–337. doi:10.1049/iet-its.2014.0288.
   Web of Science ®Google Scholar
• Tang, H., and E. T. Donnell. 2019. “Application of a model-based Recursive Partitioning Algorithm to Predict Crash Frequency.” Accident Analysis and Prevention 132 (June): 105274. doi:10.1016/j.aap.2019.105274.
   PubMedGoogle Scholar
• Tang, H., V. V. Gayah, and E. T. Donnell. 2019. “Evaluating the Predictive Power of an SPF for two-lane Rural Roads with Random Parameters on out-of-sample Observations.” Accident Analysis and Prevention 132: 105275. doi:10.1016/j.aap.2019.105275.
   PubMed Web of Science ®Google Scholar
• Wang, T., and Q. Lin. 2019. “Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model.” ArXiv 1: 1–48. http://arxiv.org/abs/1905.04241.
   Google Scholar
• Wood, J. S., E. T. Donnell, and C. J. Fariss. 2016. “A Method to Account for and Estimate Underreporting in Crash Frequency Research.” Accident Analysis & Prevention 95: 57–66. doi:10.1016/J.AAP.2016.06.013.
   PubMed Web of Science ®Google Scholar
• Wood, J. S., and R. J. Porter. 2013. “Safety Impacts of Design Exceptions on Nonfreeway Segments.” 2358 (2358): 29–37. doi:10.3141/2358-04.
   Google Scholar
• Xie, Y., D. Lord, and Y. Zhang. 2007. “Predicting Motor Vehicle Collisions Using Bayesian Neural Network Models: An Empirical Analysis.” Accident Analysis and Prevention 39 (5): 922–933. doi:10.1016/j.aap.2006.12.014.
   PubMed Web of Science ®Google Scholar
• Yehia, A., X. Wang, M. Feng, X. Yang, J. Gong, and Z. Zhu. 2021. “Applicability of Boosting Techniques in Calibrating Safety Performance Functions for Freeways.” Accident Analysis & Prevention 159: 106193. doi:10.1016/J.AAP.2021.106193.
   PubMed Web of Science ®Google Scholar
• Yu, R., and M. Abdel-Aty. 2013. “Utilizing Support Vector Machine in real-time Crash Risk Evaluation.” Accident Analysis and Prevention 51: 252–259. doi:10.1016/j.aap.2012.11.027.
   PubMed Web of Science ®Google Scholar
• Yu, R., Y. Wang, M. Quddus, and J. Li. 2019. “A Marginalized Random Effects Hurdle Negative Binomial Model for Analyzing refined-scale Crash Frequency Data.” Analytic Methods in Accident Research 22: 100092. doi:10.1016/J.AMAR.2019.100092.
   Web of Science ®Google Scholar
• Zeng, Q., H. Huang, X. Pei, S. C. Wong, and M. Gao. 2016. “Rule Extraction from an Optimized Neural Network for Traffic Crash Frequency Modeling.” Accident Analysis and Prevention 97: 87–95. doi:10.1016/j.aap.2016.08.017.
   PubMed Web of Science ®Google Scholar
• Zhang, J., Z. Li, Z. Pu, and C. Xu. 2018. “Comparing Prediction Performance for Crash Injury Severity among Various Machine Learning and Statistical Methods.” IEEE Access 6: 60079–60087. doi:10.1109/ACCESS.2018.2874979.
   Web of Science ®Google Scholar
• Zhang, X., S. T. Waller, and P. Jiang. 2020. “An Ensemble Machine Learning‐based Modeling Framework for Analysis of Traffic Crash Frequency.” Computer-Aided Civil and Infrastructure Engineering 35 (3): 258–276. doi:10.1111/mice.12485.
   Web of Science ®Google Scholar