Impact of operating speed measures on traffic crashes: Annual and daily level models for rural two-lane and rural multilane roadways

References

• Acquah, H. D. G. (2010). Comparison of Akaike information criterion (AIC) and Bayesian information criterion (BIC) in selection of an asymmetric price relationship. Journal of Development and Agricultural Economics, 2(1), 001–006.
   Google Scholar
• Ahmad, N., Ahmed, A., Wali, B., & Saeed, T. U. (2020). Exploring factors associated with crash severity on motorways in Pakistan. In Proceedings of the Institution of Civil Engineers-Transport, December (pp. 1–10). Scotland: Thomas Telford Ltd. doi:10.1680/jtran.18.00032
   Google Scholar
• Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle [Paper presentation]. In B. N. Petrov & F. Csaki (Eds.), 2nd International Symposium on Information Theory (pp. 267–81). Budapest: Akademiai Kiado.
   Google Scholar
• American Association of State Highway and Transportation Officials (AASHTO). (2010). Highway safety manual (1st ed.). Washington DC.
   Google Scholar
• Anastasopoulos, P. C., & Mannering, F. L. (2016). The effect of speed limits on drivers’ choice of speed: A random parameters seemingly unrelated equations approach. Analytic Methods in Accident Research, 10, 1–11. doi:10.1016/j.amar.2016.03.001
   Web of Science ®Google Scholar
• Bollen, K. (2014). Structural equations with latent variables. New Jersey: John Wiley & Sons.
   Google Scholar
• Carney, C., Harland, K. K., & McGehee, D. V. (2018). Examining teen driver crashes and the prevalence of distraction: Recent trends, 2007–2015. Journal of Safety Research, 64, 21–27. doi:10.1016/j.jsr.2017.12.014
   PubMed Web of Science ®Google Scholar
• Chen, S., Saeed, T. U., & Labi, S. (2017). Impact of road-surface condition on rural highway safety: A multivariate random parameters negative binomial approach. Analytic Methods in Accident Research, 16, 75–89. doi:10.1016/j.amar.2017.09.001
   Web of Science ®Google Scholar
• Chen, S., Saeed, T. U., Alinizzi, M., Lavrenz, S., & Labi, S. (2019). Safety sensitivity to roadway characteristics: a comparison across highway classes. Accident; Analysis and Prevention, 123, 39–50. doi:10.1016/j.aap.2018.10.020
   PubMed Web of Science ®Google Scholar
• Das, S. (2021). Identifying key patterns in motorcycle crashes: findings from taxicab correspondence analysis. Transportmetrica A: Transport Science, 17(4), 593–614. doi:10.1080/23249935.2020.1802362
   Web of Science ®Google Scholar
• Das, S., Dutta, A., & Rahman, M. (2021). Pattern recognition from light delivery vehicle crash characteristics. Journal of Transportation Safety & Security. https://doi.org/10.1080/19439962.2021.1995800
   Google Scholar
• Das, S., & Geedipally, S. (2020). Rural speed safety project for USDOT safety data initiative: Findings and outcome. ITE Journal, 90(9), 38–42.
   Web of Science ®Google Scholar
• Das, S., & White, L. D. (2020). RuralSpeedSafetyX: Interactive decision support tool to improve safety. SoftwareX, 11, 100493. doi:10.1016/j.softx.2020.100493
   Web of Science ®Google Scholar
• Das, S., Ashraf, S., Dutta, A., & Tran, L.-N. (2020a). Pedestrians under influence (PUI) Crashes: Patterns from correspondence regression analysis. Journal of Safety Research, 75, 14–23. doi:10.1016/j.jsr.2020.07.001
   PubMed Web of Science ®Google Scholar
• Das, S., Dutta, A., & Geedipally, S. R. (2021a). Applying Bayesian data mining to measure the effect of vehicular defects on crash severity. Journal of Transportation Safety & Security, 13(6), 605–621. doi:10.1080/19439962.2019.1658674
   Web of Science ®Google Scholar
• Das, S., Dutta, A., & Sun, X. (2020b). Patterns of rainy weather crashes: Applying rules mining. Journal of Transportation Safety & Security, 12(9), 1083–1105. doi:10.1080/19439962.2019.1572681
   Web of Science ®Google Scholar
• Das, S., Dutta, A., Kong, X., & Sun, X. (2019). Hit and run crashes: Knowledge extraction from bicycle involved crashes using first and frugal tree. International Journal of Transportation Science and Technology, 8(2), 146–160. doi:10.1016/j.ijtst.2018.11.001
   Google Scholar
• Das, S., Geedipally, S., Avelar, R., Wu, L., Fitzpatrick, K., Banihashemi, M., & Lord, D. (2020). Rural Speed Safety Project for USDOT Safety Data Initiative. Washington DC: USDOT.
   Google Scholar
• Das, S., Kong, X. J., Lavrenz, S. M., Wu, L., & Jalayer, M. (2021c). Fatal crashes at highway rail grade crossings: A U.S. based study. International Journal of Transportation Science and Technology, 11(1), 107–117. doi:10.1016/j.ijtst.2021.03.002
   Google Scholar
• Das, S., Mudgal, A., Dutta, A., & Geedipally, S. R. (2018). Vehicle consumer complaint reports involving severe incidents: Mining large contingency tables. Transportation Research Record: Journal of the Transportation Research Board, 2672(32), 72–82. doi:10.1177/0361198118788464
   Web of Science ®Google Scholar
• Das, S., Sun, X., Goel, S., Sun, M., Rahman, A., & Dutta, A. (2022). Flooding related traffic crashes: Findings from association rules. Journal of Transportation Safety & Security, 14(1), 111–129. doi:10.1080/19439962.2020.1734130
   Web of Science ®Google Scholar
• Das, S., Wei, Z., Kong, X., & Xiao, X. (2021d). Mining crowdsourced data on bicycle safety critical events. Transportation Research Interdisciplinary Perspectives, 10, 100360. doi:10.1016/j.trip.2021.100360
   Google Scholar
• Dimaiuta, M., Banihashemi, M., Spear, S., Hull, R., Beer, P., Smadi, O., & Zach Hans, Z. (2018). Speed–safety analyses using linked national performance management research data set (NPMRDS) and SHRP2 Roadway Information Database (RID) Data. Washington DC: FHWA.
   Google Scholar
• Dimitriou, L., Stylianou, K., & Abdel-Aty, M. A. (2018). Assessing rear-end crash potential in urban locations based on vehicle-by-vehicle interactions, geometric characteristics and operational conditions. Accident; Analysis and Prevention, 118, 221–235. doi:10.1016/j.aap.2018.02.024
   PubMed Web of Science ®Google Scholar
• Dixon, K. (2022). Developing safety performance functions for rural two-lane highways that incorporate speed measures. NCHRP Research Project. https://apps.trb.org/cmsfeed/TRBNetProjectDisplay.asp?ProjectID=4574
   Google Scholar
• Dutta, N., & Fontaine, M. D. (2019). Improving freeway segment crash prediction models by including disaggregate speed data from different sources. Accident Analysis & Prevention, 132, 105253. doi:10.1016/j.aap.2019.07.029
   PubMed Web of Science ®Google Scholar
• Ederer, D. J., Rodgers, M. O., Hunter, M. P., & Watkins, K. E. (2020). Case study using probe vehicle speeds to assess roadway safety in Georgia. Transportation Research Record: Journal of the Transportation Research Board, 2674(11), 554–562. doi:10.1177/0361198120948858
   Web of Science ®Google Scholar
• Elvik, R. (2014). Speed and road safety - new models. Institute of Transport Economics (TØI) (Fart og trafikksikkerhet - Nye modeller), Norway.
   Google Scholar
• Fitzpatrick, K., Das, S. (2019). Vehicle operating speed on urban arterial roadways. https://www.vtti.vt.edu/utc/safe-d/wp-content/uploads/2019/04/TTI-01-04_Final-Research-Report.pdf.
   Google Scholar
• Fitzpatrick, K., Das, S., Pratt, M. P., Dixon, K., & Gates, T. (2021a). Posted speed limit setting procedure and tool: User guide. Washington DC: Transportation Research Board.
   Google Scholar
• Fitzpatrick, K., Das, S., Pratt, M. P., Dixon, K., & Gates, T. (2021b). Development of a posted speed limit setting procedure and tool. Washington DC: Transportation Research Board.
   Google Scholar
• Gargoum, S. A., & El-Basyouny, K. (2016). Exploring the association between speed and safety: A path analysis approach. Accident; Analysis and Prevention, 93, 32–40. doi:10.1016/j.aap.2016.04.029
   PubMed Web of Science ®Google Scholar
• Gonzales, D. E., Fontaine, M. D., & Dutta, N. (2019). Impact of variable speed-limit system on driver speeds during low-visibility conditions. Journal of Transportation Engineering, Part A: Systems, 145(12), 04019050. doi:10.1061/JTEPBS.0000282
   Web of Science ®Google Scholar
• Hauer, E. (2009). Speed and safety. Transportation Research Record: Journal of the Transportation Research Board, 2103(1), 10–17. doi:10.3141/2103-02
   Web of Science ®Google Scholar
• Hossain, F., & Medina, J. C. (2020). Effects of operating speed and traffic flow on severe and fatal crashes using the U.S. road assessment program methodology and field data verification. Transportation Research Record: Journal of the Transportation Research Board, 2674(11), 30–41. doi:10.1177/0361198120943587
   Web of Science ®Google Scholar
• Hutton, J. M. (2020). Research utilizing SHRP2 data to improve highway safety: Development of speed–safety relationships (p. 110). https://www.fhwa.dot.gov/publications/research/safety/20035/20035.pdf.
   Google Scholar
• Imprialou, M.-I. M., Quddus, M., Pitfield, D. E., & Lord, D. (2016). Re-visiting crash–speed relationships: A new perspective in crash modelling. Accident; Analysis and Prevention, 86, 173–185.
   PubMed Web of Science ®Google Scholar
• Kass, R. E., & Raftery, A. (1995). Bayes factors. Journal of the American Statistical Association, 90(430), 773–795. doi:10.1080/01621459.1995.10476572
   Web of Science ®Google Scholar
• Kong, X., Das, S., Zhou, H. “., & Zhang, Y. (2021a). Patterns of near-crash events in a naturalistic driving dataset: Applying rules mining. Accident Analysis & Prevention, 161, 106346. doi:10.1016/j.aap.2021.106346
   PubMed Web of Science ®Google Scholar
• Kong, X., Das, S., Zhou, H., & Zhang, Y. (2021b). Characterizing phone usage while driving: Safety impact from road and operational perspectives using factor analysis. Accident; Analysis and Prevention, 152, 106012. doi:10.1016/j.aap.2021.106012
   PubMed Web of Science ®Google Scholar
• Liu, T., Yang, Y., Huang, G.-B., Lin, Z., Klanner, F., Denk, C., & Rasshofer, R. H. Cluster regularized extreme learning machine for detecting mixed-type distraction in driving. 18th International IEEE Conference on Intelligent Transportation Systems (ITSC), Spain.
   Google Scholar
• Musicant, O., Bar-Gera, H., & Schechtman, E. (2016). Impact of speed limit change on driving speed and road safety on interurban roads: Meta-analysis. Transportation Research Record: Journal of the Transportation Research Board, 2601(1), 42–49. doi:10.3141/2601-06
   Google Scholar
• Muthen, L., & Muthen, B. (2017). Mplus user’s guide (8th ed.). Los Angeles, CA.
   Google Scholar
• National Highway Traffic Safety Administration (NHTSA). (2020). Fatality analysis reporting system encyclopedia. Washington DC. https://www-fars.nhtsa.dot.gov/Main/index.aspx
   Google Scholar
• Newnam, S., Mulvihill, C., & Muir, C. (2020). Safety when operating on high-speed roads: Protecting our incident responders. Safety Science, 131, 104910. doi:10.1016/j.ssci.2020.104910
   Web of Science ®Google Scholar
• Park, E. S., Fitzpatrick, K., Das, S., & Avelar, R. (2021). Exploration of the relationship among roadway characteristics, operating speed, and crashes for city streets using path analysis. Accident; Analysis and Prevention, 150, 105896. doi:10.1016/j.aap.2020.105896
   PubMed Web of Science ®Google Scholar
• Pratt, M. P., Geedipally, S. R., & Le, M. (2020). Cross-sectional study of vehicle speeds on rural four-lane highway curves. Transportation Research Record: Journal of the Transportation Research Board, 2674(10), 15–27. doi:10.1177/0361198120939096
   Web of Science ®Google Scholar
• Rahman, M. A., Sun, X., Das, S., & Khanal, S. (2021). Exploring the influential factors of roadway departure crashes on rural two-lane highways with logit model and association rules mining. International Journal of Transportation Science and Technology, 10(2), 167–183. doi:10.1016/j.ijtst.2020.12.003
   Google Scholar
• Saeed, T. U., Burris, M. W., Labi, S., & Sinha, K. C. (2020a). An empirical discourse on forecasting the use of autonomous vehicles using consumers’ preferences. Technological Forecasting and Social Change, 158, 120130. doi:10.1016/j.techfore.2020.120130
   Web of Science ®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. doi:10.1016/j.amar.2019.100101
   Web of Science ®Google Scholar
• Saeed, T. U., Nateghi, R., Hall, T., & Waldorf, B. S. (2020b). Statistical analysis of area‐wide alcohol‐related driving crashes: a spatial econometric approach. Geographical Analysis, 52(3), 394–417. doi:10.1111/gean.12216
   Web of Science ®Google Scholar
• Saeed, T. U., Qiao, Y., Chen, S., Gkritza, K., & Labi, S. (2017). Methodology for probabilistic modeling of highway bridge infrastructure condition: Accounting for improvement effectiveness and incorporating random effects. Journal of Infrastructure Systems, 23(4), 04017030. doi:10.1061/(ASCE)IS.1943-555X.0000389
   Web of Science ®Google Scholar
• Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461–464.
   Web of Science ®Google Scholar
• van Schagen, I., et al. (2018). Speed and speed management. Washington DC: FHWA. https://ec.europa.eu/transport/road_safety/sites/roadsafety/files/pdf/ersosynthesis2018-detail-speedspeedmanagement32_en.pdf.
   Google Scholar
• Volovski, M., Grillo, N., Varga, C., Saeed, T. U., & El-Hakim, M. (2021). Subway ridership: Accounting for regional variation across land-use and socioeconomic settings. Journal of Infrastructure Systems, 27(2), 04021010. doi:10.1061/(ASCE)IS.1943-555X.0000613
   Web of Science ®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.
   PubMed Web of Science ®Google Scholar
• Wen, F., Zhang, G., Sun, L., Wang, X., & Xu, X. (2019). A hybrid temporal association rules mining method for traffic congestion prediction. Computers & Industrial Engineering, 130, 779–787. doi:10.1016/j.cie.2019.03.020
   Web of Science ®Google Scholar
• Wu, L., Dadashova, B., Geedipally, S., Pratt, M. P., & Shirazi, M. (2021). Using naturalistic driving study data to explore the association between horizontal curve safety and operation on rural two-lane highways. Journal of Transportation Safety & Security, 13(8), 896–913. doi:10.1080/19439962.2019.1697776
   Web of Science ®Google Scholar