Coping with endogeneity and unobserved heterogeneity in real-time clustering critical crash occurrences nested within weather and road surface conditions

References

• Adomah, E., Khoda Bakhshi, A., & Ahmed, M. M. (2021). Safety impact of connected vehicles on driver behavior in rural work zones under foggy weather conditions [Paper presentation]. Transportation Research Board. 100th Annual Meeting, Washington, DC. Retrieved from https://trid.trb.org/view/1759061
   Google Scholar
• Ahmed, M. M., & Abdel-Aty, M. A. (2012). The viability of using automatic vehicle identification data for real-time crash prediction. IEEE Transactions on Intelligent Transportation Systems, 13(2), 459–468. https://doi.org/10.1109/TITS.2011.2171052
   Web of Science ®Google Scholar
• Anastasopoulos, P. C., & Mannering, F. L. (2009). A note on modeling vehicle accident frequencies with random-parameters count models. Accident; Analysis and Prevention, 41(1), 153–159. https://doi.org/10.1016/j.aap.2008.10.005
   PubMed Web of Science ®Google Scholar
• Anastasopoulos, P. C., Mannering, F. L., Shankar, V. N., & Haddock, J. E. (2012). A study of factors affecting highway accident rates using the random-parameters Tobit model. Accident; Analysis and Prevention, 45, 628–633. https://doi.org/10.1016/j.aap.2011.09.015
   PubMed Web of Science ®Google Scholar
• Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2014). Causality and endogeneity: Problems and solutions. In D. Day (Ed.), The Oxford handbook of leadership and organizations (pp. 93–117). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199755615.013.007
   Google Scholar
• Azizi, L., & Hadi, M. (2021). Utilizing disturbance metrics in traffic safety estimation. Proceedings of the Institution of Civil Engineers-Transport, 1–33. https://doi.org/10.1680/jtran.20.00080
   Google Scholar
• Bakhtiari, S., Zhang, T., Zafian, T., Samuel, S., Knodler, M., Fitzpatrick, C., & Fisher, D. L. (2019). Effect of visual and auditory alerts on older drivers’ glances toward latent hazards while turning left at intersections. Transportation Research Record: Journal of the Transportation Research Board, 2673(9), 117–126. https://doi.org/10.1177/0361198119844244
   Web of Science ®Google Scholar
• Browne, W. J., & Draper, D. (2006). A comparison of Bayesian and likelihood-based methods for fitting multilevel models. Bayesian Analysis, 1(3), 473–514. https://doi.org/10.1214/06-BA117
   Web of Science ®Google Scholar
• Chiou, Y.-C., & Fu, C. (2015). Modeling crash frequency and severity with spatiotemporal dependence. Analytic Methods in Accident Research, 5-6, 43–58. https://doi.org/10.1016/j.amar.2015.03.002
   Web of Science ®Google Scholar
• Collins, L. M., & Seitz, L. A. (1994). Advances in data analysis for prevention intervention research. US Department of Health and Human Services, Public Health Service, National Institutes of Health.
   Google Scholar
• Crunkleton, J. L., Tarnoff, P. J. (2015). Statistical patterns of traffic data and sample size estimation (Vol. 2015). Center for Advanced Transportation Technology. Retrieved from http://www.catt.umd.edu/sites/default/files/documents/variance_analysis_v1.pdf
   Google Scholar
• Das, S., Mousavi, S. M., & Shirinzad, M. (2021). Pattern recognition in speeding related motorcycle crashes. Journal of Transportation Safety & Security, 1–18. https://doi.org/10.1080/19439962.2021.1877228
   Google Scholar
• Eftekharzadeh, S. F., & Khodabakhshi, A. (2014). Safety evaluation of highway geometric design criteria in horizontal curves at downgrades. International Journal of Civil Engineering, 12(3), 326–332. Retrieved from http://ijce.iust.ac.ir/article-1-833-en.html
   Web of Science ®Google Scholar
• Eisenberg, D., & Warner, K. E. (2005). Effects of snowfalls on motor vehicle collisions, injuries, and fatalities. American Journal of Public Health, 95(1), 120–124. https://doi.org/10.2105/AJPH.2004.048926
   PubMed Web of Science ®Google Scholar
• Fournier, N., Bakhtiari, S., Valluru, K. D., Campbell, N., Christofa, E., Roberts, S., & Knodler, M. (2020). Accounting for drivers’ bicycling frequency and familiarity with bicycle infrastructure treatments when evaluating safety. Accident Analysis & Prevention, 137, 105410. https://doi.org/10.1016/j.aap.2019.105410
   PubMed Web of Science ®Google Scholar
• Gaweesh, S. M., Khoda Bakhshi, A., & Ahmed, M. M. (2021). Safety performance assessment of connected vehicles in mitigating the risk of secondary crashes: A driving simulator study [Paper presentation]. Transportation Research Board. 100th Annual Meeting, Washington, DC. Retrieved from https://trid.trb.org/view/1760006
   Google Scholar
• Gelman, A. (2006). Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper). Bayesian Analysis, 1(3), 515–534. https://doi.org/10.1214/06-BA117A
   Web of Science ®Google Scholar
• Gelman, A., & Hill, J. (2006). Data analysis using regression and multilevel/hierarchical models. Cambridge University Press. Retrieved from https://doc1.bibliothek.li/acc/flmf044788.pdf
   Google Scholar
• Gopalakrishna, D., Garcia, V., Ragan, A., English, T., Zumpf, S., Young, R., … Hsu, E. (2015). Connected vehicle pilot deployment program phase 1, concept of operations (ConOps), ICF/Wyoming. Retrieved from United States. Department of Transportation. Intelligent Transportation … website: https://rosap.ntl.bts.gov/view/dot/3582
   Google Scholar
• Guo, G., & Zhao, H. (2000). Multilevel modeling for binary data. Annual Review of Sociology, 26(1), 441–462. https://doi.org/10.1146/annurev.soc.26.1.441
   Web of Science ®Google Scholar
• Hackshaw, A. (2008). Small studies: strengths and limitations. Eur Respiratory Soc.
   Google Scholar
• Hakim, S., Nekoomanesh, M., & Shahrokhinia, A. (2015). The effect of mixed and individual silane external donors on the stereo-defect distribution, active sites and properties of polypropylene synthesized with fourth generation Ziegler—Natta catalyst. Polymer Science Series A, 57(5), 573–580. https://doi.org/10.1134/S0965545X15050090
   Web of Science ®Google Scholar
• Hirunyanitiwattana, W., & Mattingly, S. P. (2006). Identifying secondary crash characteristics for California highway system [Paper Presentation]. Transportation Research Board. 85th Annual Meeting, Washington DC.
   Google Scholar
• Hoffman, M. D., & Gelman, A. (2014). The No-U-Turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research, 15(1), 1593–1623. Retrieved from http://www.jmlr.org/papers/volume15/hoffman14a/hoffman14a.pdf
   Google Scholar
• Hossain, M., Abdel-Aty, M., Quddus, M. A., Muromachi, Y., & Sadeek, S. N. (2019). Real-time crash prediction models: State-of-the-art, design pathways and ubiquitous requirements. Accident; Analysis and Prevention, 124, 66–84. https://doi.org/10.1016/j.aap.2018.12.022
   PubMed Web of Science ®Google Scholar
• Hosseinzadeh, A., & Kluger, R. (2021). Do EMS times associate with injury severity? Accident; Analysis and Prevention, 153, 106053. https://doi.org/10.1016/j.aap.2021.106053
   PubMed Web of Science ®Google Scholar
• Hosseinzadeh, A., Haghani, M., & Kluger, R. (2021). Exploring influencing factors on crash-related emergency response time: A machine learning approach [Paper presentation]. Transportation Research Board. 100th Annual Meeting, Washington, DC.
   Google Scholar
• Hosseinzadeh, A., Karimpour, A., Kluger, R., & Orthober, R. (2020). A framework to link crashes to emergency medical service runs and trauma admissions: For improved highway safety monitoring and crash outcome assessment [Paper presentation]. Transportation Research Board. 99th Annual Meeting, Washington, DC.
   Google Scholar
• Hosseinzadeh, A., Moeinaddini, A., & 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. https://doi.org/10.1016/j.jsr.2021.02.012
   Google Scholar
• Huang, H., & Abdel-Aty, M. (2010). Multilevel data and Bayesian analysis in traffic safety. Accident; Analysis and Prevention, 42(6), 1556–1565. https://doi.org/10.1016/j.aap.2010.03.013
   PubMed Web of Science ®Google Scholar
• Jones, A. P., & Jørgensen, S. H. (2003). The use of multilevel models for the prediction of road accident outcomes. Accident; Analysis and Prevention, 35(1), 59–69. https://doi.org/10.1016/S0001-4575(01)00086-0
   PubMed Web of Science ®Google Scholar
• Kashgarani, H., & Kotthoff, L. (2021) Is algorithm selection worth it? Comparing selecting single algorithms and parallel execution, Proceedings of 2021 AAAI Workshop on Meta-Learning and MetaDL Challenge, Proceedings of Machine Learning Research (PMLR), Vancouver, Canada,
   Google Scholar
• Khan, G., Qin, X., & Noyce, D. A. (2008). Spatial analysis of weather crash patterns. Journal of Transportation Engineering, 134(5), 191–202. https://doi.org/10.1061/(ASCE)0733-947X(2008)134:5(191)
   Web of Science ®Google Scholar
• Khattak, A., Ahmad, N., Mohammadnazar, A., MahdiNia, I., Wali, B., & Arvin, R. (2020). Highway safety manual safety performance functions & roadway calibration factors: Roadway segments phase 2, Part 1. Report no. RES 2016-27, Tennessee Department of Transportation (TDOT), Tennessee.
   Google Scholar
• Khoda Bakhshi, A., & Ahmed, M. M. (2021b). Bayesian extreme value analysis of kinematic-based surrogate measure of safety to detect crash-prone conditions in connected vehicles environment: A driving simulator experiment. Transportation Research Part C: Emerging Technology (in press).
   Google Scholar
• Khoda Bakhshi, A., & Ahmed, M. M. (2021c). Does random slope hierarchical modeling always outperform random intercept counterpart? Accounting for unobserved heterogeneity in a real-time empirical analysis of critical crash occurrence. Accident Analysis and Prevention (in press).
   Google Scholar
• Khoda Bakhshi, A., & Ahmed, M. M. (2021d). Application of cross-classified random-effects modeling to account for unobserved heterogeneity in real-time clustering critical versus non-critical crashes. [Paper presentation]. ASCE, International Conference on Transportation & Development, Austin, TX.
   Google Scholar
• Khoda Bakhshi, A., & Ahmed, M. M. (2021e). Accounting for human-related unobserved heterogeneity to enhance the safety performance of work zone warning application in connected vehicles environment [Paper presentation]. ASCE, International Conference on Transportation & Development, Austin, TX.
   Google Scholar
• Khoda Bakhshi, A., & Ahmed, M. M. (2021f). Real-time crash prediction for a long low-traffic volume corridor using corrected-impurity importance and semi-parametric generalized additive model [Paper presentation]. ASCE, International Conference on Transportation & Development,Austin, TX.
   Google Scholar
• Khoda Bakhshi, A., & Ahmed, M. M. (2020a). Practical advantage of crossed random intercepts under Bayesian hierarchical modeling to tackle unobserved heterogeneity in clustering critical versus non-critical crashes. Accident; Analysis and Prevention, 149, 105855. https://doi.org/10.1016/j.aap.2020.105855
   PubMed Web of Science ®Google Scholar
• Khoda Bakhshi, A., & Ahmed, M. M. (2020b). Real-time crash prediction for a long low-traffic volume corridor using corrected-impurity importance and semi-parametric generalized additive model. Journal of Transportation Safety & Security. https://doi.org/10.1080/19439962.2021.1898069
   Web of Science ®Google Scholar
• Khoda Bakhshi, A., & Ahmed, M. M. (2020c). Utilizing black-box visualization tools to interpret non-parametric real-time risk assessment models. Transportmetrica A: Transport Science, 17(4), 739–765. https://doi.org/10.1080/23249935.2020.1810169
   Web of Science ®Google Scholar
• Khoda Bakhshi, A., & Ahmed, M. M. (2021a). A note on random forest visualization tools in post-hoc interpretation of nonparametric real-time risk assessment models [Paper presentation]. Transportation Research Board, 100th Annual Meeting, Washington, DC.
   Google Scholar
• Khoda Bakhshi, A., Ahmed, M. M., & Adomah, E. (2021a). Accounting for human-related unobserved heterogeneity in the safety performance of connected vehicles work zone warning application. IATSS Research (in press).
   Google Scholar
• Khoda Bakhshi, A., Gaweesh, S. M., & Ahmed, M. M. (2021b). The safety performance of connected vehicles on slippery horizontal curves through enhancing truck drivers’ situational awareness: A driving simulator experiment. Transportation Research Part F: Traffic Psychology and Behaviour (in press).
   PubMedGoogle Scholar
• Khoda Bakhshi, A., Gaweesh S. M., & Ahmed, M. M. (2021c). Mitigating the risk of horizontal curve-related crashes via altering drivers’ behaviors in connected vehicles environment[Paper presentation]. ASCE, international conference on transportation & development. Austin, TX.
   Google Scholar
• Kim, D.-G., Lee, Y., Washington, S., & Choi, K. (2007). Modeling crash outcome probabilities at rural intersections: Application of hierarchical binomial logistic models. Accident; Analysis and Prevention, 39(1), 125–134. https://doi.org/10.1016/j.aap.2006.06.011
   PubMed Web of Science ®Google Scholar
• Koloushani, M. R., Fatemi, A., & Tabibi, M. (2014). Application of global positioning system data collected by mobile mapping system for automatic control of safety standards in horizontal curves. Transportation Research Board, 93rd Annual Meeting, Washington, DC. Retrieved from https://trid.trb.org/view/1287757
   Google Scholar
• Koloushani, M., Fatemi, A., Tabibi, M., & Ozguven, E. E. (2020). Introducing automatic control method of safety standards in horizontal curves by processing images taken by mobile mapping system. Pertanika Journal of Social Sciences & Humanities, 28(2), 1567–1579.
   Web of Science ®Google Scholar
• Lamm, R., Choueiri, E. M., & Mailaender, T. (1990). Comparison of operating speeds on dry and wet pavements of two-lane rural highways. Transportation Research Record, 1280(8), 199–207. Retrieved from https://pdfs.semanticscholar.org/4fbe/0962311c202815f80fa283c49af79ad7f918.pdf
   Google Scholar
• Li, Z., Chen, X., Ci, Y., Chen, C., & Zhang, G. (2019). A hierarchical Bayesian spatiotemporal random parameters approach for alcohol/drug impaired-driving crash frequency analysis. Analytic Methods in Accident Research, 21, 44–61. https://doi.org/10.1016/j.amar.2019.01.002
   Web of Science ®Google Scholar
• Lord, D., & Mannering, F. (2010). The statistical analysis of crash-frequency data: a review and assessment of methodological alternatives. Transportation Research Part A: Policy and Practice, 44(5), 291–305. https://doi.org/10.1016/j.tra.2010.02.001
   Web of Science ®Google Scholar
• Mannering, F. L., & Bhat, C. R. (2014). Analytic methods in accident research: Methodological frontier and future directions. Analytic Methods in Accident Research, 1, 1–22. https://doi.org/10.1016/j.amar.2013.09.001
   Google Scholar
• Mannering, F. L., 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
   Web of Science ®Google Scholar
• Mannering, F., Bhat, C. R., Shankar, V., & Abdel-Aty, M. (2020). Big data, traditional data and the tradeoffs between prediction and causality in highway-safety analysis. Analytic Methods in Accident Research, 25, 100113. https://doi.org/10.1016/j.amar.2020.100113
   Web of Science ®Google Scholar
• Mansourkhaki, A., Karimpour, A., & Sadoghi Yazdi, H. (2017). Non-stationary concept of accident prediction. Proceedings of the Institution of Civil Engineers - Transport, 170(3), 140–151. https://doi.org/10.1680/jtran.15.00053
   Web of Science ®Google Scholar
• Mansourkhaki, A., Karimpour, A., & Yazdi, H. S. (2017). Introducing prior knowledge for a hybrid accident prediction model. KSCE Journal of Civil Engineering, 21(5), 1912–1918. https://doi.org/10.1007/s12205-016-0495-4
   Web of Science ®Google Scholar
• McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and Stan. CRC Press. https://doi.org/10.1207/s15327906mbr4104_3
   Google Scholar
• McNeish, D. (2016). On using Bayesian methods to address small sample problems. Structural Equation Modeling: A Multidisciplinary Journal, 23(5), 750–773. https://doi.org/10.1080/10705511.2016.1186549
   Web of Science ®Google Scholar
• Mohammadnazar, A., Mahdinia, I., Ahmad, N., Khattak, A. J., & Liu, J. (2021). Understanding how relationships between crash frequency and correlates vary for multilane rural highways: Estimating geographically and temporally weighted regression models. Accident Analysis & Prevention (in press).
   PubMedGoogle Scholar
• Mousavi, S. M., Osman, O. A., Lord, D., Dixon, K. K., & Dadashova, B. (2021). Investigating the safety and operational benefits of mixed traffic environments with different automated vehicle market penetration rates in the proximity of a driveway on an urban arterial. Accident Analysis & Prevention, 152, 105982. https://doi.org/10.1016/j.aap.2021.105982
   PubMed Web of Science ®Google Scholar
• Muthén, B., & Asparouhov, T. (2012). Bayesian structural equation modeling: a more flexible representation of substantive theory. Psychological Methods, 17(3), 313–335. https://doi.org/10.1037/a0026802
   PubMed Web of Science ®Google Scholar
• Nembrini, S., König, I. R., & Wright, M. N. (2018). The revival of the Gini importance? Bioinformatics (Oxford, England), 34(21), 3711–3718. https://doi.org/10.1093/bioinformatics/bty373
   PubMed Web of Science ®Google Scholar
• Neter, J., Kutner, M. H., Nachtsheim, C. J., & Wasserman, W. (1996). Applied linear statistical models. (Vol. 4). WCB McGraw-Hill.
   Google Scholar
• O’Dwyer, L. M., Parker, C. E. (2014). A primer for analyzing nested data: multilevel modeling in SPSS using an example from a REL study. In National Center for Education Statistics. Retrieved from https://files.eric.ed.gov/fulltext/ED551064.pdf
   Google Scholar
• Oh, C., Oh, J.-S., Ritchie, S., & Chang, M. (2001). Real-time estimation of freeway accident likelihood [Paper presentation]. 80th Annual Meeting of the Transportation Research Board, Washington, DC.
   Google Scholar
• Pisano, P. A., Goodwin, L. C., & Rossetti, M. A. (2008). US highway crashes in adverse road weather conditions [Paper presentation]. 24th Conference on International Interactive Information and Processing Systems for Meteorology, Oceanography and Hydrology, New Orleans, LA. Retrieved from https://www.researchgate.net/profile/Paul_Pisano/publication/253409103_US_highway_crashes_in_adverse_road_weather_conditions/links/0c9605295f512da266000000.pdf
   Google Scholar
• Sadeghinasr, B., Akhavan, A., & Wang, Q. (2019). Estimating commuting patterns from high resolution phone GPS data. ASCE International Conference on Computing in Civil Engineering, Atlanta, Georgia, June 17–19.
   Google Scholar
• Saeed, T. U., Hall, T., Baroud, H., & Volovski, M. J. (2019). Analyzing road crash frequencies with uncorrelated and correlated random-parameters count models: An empirical assessment of multilane highways. Analytic Methods in Accident Research, 23, 100101. https://doi.org/10.1016/j.amar.2019.100101
   Web of Science ®Google Scholar
• Schönbrodt, F. D., & Perugini, M. (2013). At what sample size do correlations stabilize? Journal of Research in Personality, 47(5), 609–612. https://doi.org/10.1016/j.jrp.2013.05.009
   Web of Science ®Google Scholar
• Shahrokhinia, A., Scanga, R. A., Biswas, P., & Reuther, J. F. (2021). PhotoATRP-Induced Self-Assembly (PhotoATR-PISA) enables simplified synthesis of responsive polymer nanoparticles in one-pot. Macromolecules, 54(3), 1441–1451. https://doi.org/10.1021/acs.macromol.0c02106
   Web of Science ®Google Scholar
• Shieh, G. (2016). Choosing the best index for the average score intraclass correlation coefficient. Behavior Research Methods, 48(3), 994–1003. https://doi.org/10.3758/s13428-015-0623-y
   PubMed Web of Science ®Google Scholar
• Sommet, N., & Morselli, D. (2017). Keep calm and learn multilevel logistic modeling: A simplified three-step procedure using Stata, R, Mplus, and SPSS. International Review of Social Psychology, 30(1), 203–218. https://doi.org/10.5334/irsp.90
   Web of Science ®Google Scholar
• Strobl, C., Boulesteix, A.-L., Kneib, T., Augustin, T., & Zeileis, A. (2008). Conditional variable importance for random forests. BMC Bioinformatics, 9(1), 307. https://doi.org/10.1186/1471-2105-9-307
   PubMed Web of Science ®Google Scholar
• Tabibi, M., Koloushani, M. R., & Fatemi, A. (2016). Introducing automatic control method of speed limit signs safety standards before horizontal curves via mobile mapping system. Amirkabir Journal of Civil Engineering, 48, 301–314.
   Google Scholar
• Valen, A., Bogstrand, S. T., Vindenes, V., Frost, J., Larsson, M., Holtan, A., & Gjerde, H. (2019). Driver-related risk factors of fatal road traffic crashes associated with alcohol or drug impairment. Accident; Analysis and Prevention, 131, 191–199. https://doi.org/10.1016/j.aap.2019.06.014
   PubMed Web of Science ®Google Scholar
• Van de Schoot, R., Kaplan, D., Denissen, J., Asendorpf, J. B., Neyer, F. J., & Van Aken, M. A. G. (2014). A gentle introduction to Bayesian analysis: Applications to developmental research. Child Development, 85(3), 842–860. https://doi.org/10.1111/cdev.12169
   PubMed Web of Science ®Google Scholar
• Veeramisti, N., Paz, A., & Baker, J. (2020). A framework for corridor-level traffic safety network screening and its implementation using Business Intelligence. Safety Science, 121, 100–110. https://doi.org/10.1016/j.ssci.2019.08.042
   Web of Science ®Google Scholar
• Wali, B., Ahmad, N., Khattak, A. J., & Mohammadnazar, A. (2020). Developing safety performance functions for rural multilane highways in Tennessee: Accounting for unobserved heterogeneity [Paper presentation]. Transportation Research Board 99th Annual Meeting Transportation Research Board, Washington, DC.
   Google Scholar
• Waseem, M., Ahmed, A., & Saeed, T. U. (2019). Factors affecting motorcyclists' injury severities: An empirical assessment using random parameters logit model with heterogeneity in means and variances. Accident; Analysis and Prevention, 123, 12–19. https://doi.org/10.1016/j.aap.2018.10.022
   PubMed Web of Science ®Google Scholar
• Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. MIT Press.
   Google Scholar
• Wyoming Department of Transportation. (2018). Master Plan Implementation Report, I-80 Corridor Study. Retrieved from http://www.dot.state.wy.us/files/live/sites/wydot/files/shared/Planning/Transportation-Plans/Final-Implementation-ReportwAppendices_FinalMarch2018.pdf
   Google Scholar
• Xu, C., Liu, P., Yang, B., & Wang, W. (2016). Real-time estimation of secondary crash likelihood on freeways using high-resolution loop detector data. Transportation Research Part C: Emerging Technologies, 71, 406–418. https://doi.org/10.1016/j.trc.2016.08.015
   Web of Science ®Google Scholar