Crash Severity Analysis for Low-Speed Roads Using Structural Equation Modeling Considering Shoulder- and Pavement-Distress Conditions
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 146, Issue 7
Abstract
Crash severity outcomes are random and are influenced by the interactions of vehicle, driver, crash, road, and environmental factors. Limited research has attempted to assess the direct and latent influence of road factors like pavement and shoulder conditions, crash and vehicle factors, along with weather and driver factors on crash severity outcomes. This work attempts to develop crash severity prediction models considering the direct and indirect influence of road factors measured with pavement distress conditions, shoulder type and condition, crash factors measured with crash type and collision partners, human and weather factors measured with crash time (for visibility and traffic condition), season of crash occurrence, and driver age and gender. The work models crash severity outcomes using the commonly used Ordered Probit model, which recognizes the inherent ordered nature of crash severity outcomes, and also using Structured Equation Modeling (SEM), which not only considers the direct influences of the predictor variables but also their unobserved or latent influence. The Ordered Probit model was developed to assess discrete change probabilities for each factor for each severity level outcomes. The direct and indirect influence of individual factors was analyzed in detail using the calibrated SEM model. It could be observed that pavement distress condition, shoulder type and condition, crash type, and collision partners play an important role in the determination of the severity level outcomes of crashes. The overall model fit for Ordered Probit was not significant, but the SEM calibrated model was significant, indicating that the SEM model can be calibrated reasonably with smaller crash datasets.
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Data Availability Statement
All data, models, or code generated or used during the study are available from the corresponding author by request
1.
Crash data of study road segments.
2.
Pavement and shoulder condition data of study road segments.
Acknowledgments
The authors would like to acknowledge the Traffic Police Department of Patna for sharing the crash data. The authors would also like to acknowledge the efforts of M Tech scholars Mr. Abhishek Kumar, and Mr. Praloy Das of Department of Civil Engineering, National Institute of Technology Patna for their active support during the survey.
References
ASTM. 2015. Standard practice for roads and parking lots pavement condition index surveys. Philadesphia, PA: ASTM.
Atnafu, B., and G. Kaur. 2017. “Survey on analysis and prediction of road traffic accident severity levels using data mining techniques in Maharashtra, India.” Int. J. Curr. Eng. Technol. 7 (6): 1–6.
Bogue, S., R. Paleti, and L. Balan. 2017. “A modified rank ordered logit model to analyse injury severity of occupants in multivehicle crashes.” Anal. Methods Accid. Res. 14 (Jun): 22–40. https://doi.org/10.1016/j.amar.2017.03.001.
Chang, L.-Y., and H.-W. Wang. 2006. “Analysis of traffic injury severity: An application of non-parametric classification tree techniques.” Accid. Anal. Prev. 38 (5): 1019–1027. https://doi.org/10.1016/j.aap.2006.04.009.
Cheng, W., G. S. Gill, T. Sakrani, M. Dasu, and J. Zhou. 2017. “Predicting motorcycle crash injury severity using weather data and alternative Bayesian multivariate crash frequency models.” Accid. Anal. Prev. 108 (Nov): 172–180. https://doi.org/10.1016/j.aap.2017.08.032.
Dadashova, B., B. A. Ramírez, J. M. McWilliams, and F. A. e Izquierdo. 2016. “The identification of patterns of interurban road accident frequency and severity using road geometry and traffic indicators.” Transp. Res. Procedia 14 (979): 4122–4129. https://doi.org/10.1016/j.trpro.2016.05.383.
Fountas, G., and P. C. Anastasopoulos. 2018. “Analysis of accident injury-severity outcomes: The zero-inflated hierarchical Ordered Probit model with correlated disturbances.” Anal. Methods Accid. Res. 20 (Dec): 30–45. https://doi.org/10.1016/j.amar.2018.09.002.
Garrido, R., A. Bastos, A. D. Almeida, and J. P. Elvas. 2014. “Prediction of road accident severity using the ordered probit model.” Transp. Res. Procedia 3 (Jan): 214–223. https://doi.org/10.1016/j.trpro.2014.10.107.
Gitelman, V., E. Doveh, R. Carmel, and S. Hakkert. 2019. “The influence of shoulder characteristics on the safety level of two-lane roads: A case-study.” Accid. Anal. Prev. 122 (Jan): 108–118. https://doi.org/10.1016/j.aap.2018.10.003.
Gray, R. C., M. A. Quddus, and A. Evans. 2008. “Injury severity analysis of accidents involving young male drivers in Great Britain.” J. Saf. Res. 39 (5): 483–495. https://doi.org/10.1016/j.jsr.2008.07.003.
Hu, S.-R., C.-S. Li, and C.-K. Lee. 2010. “Investigation of key factors for accident severity at railroad grade crossings by using a logit model.” Saf. Sci. 48 (2): 186–194. https://doi.org/10.1016/j.ssci.2009.07.010.
Kockelman, K. M., and Y. J. Kweon. 2002. “Driver injury severity: An application of ordered probit models.” Accid. Anal. Prev. 34 (3): 313–321. https://doi.org/10.1016/S0001-4575(01)00028-8.
Lee, J., J. Chae, T. Yoon, and H. Yang. 2018. “Traffic accident severity analysis with rain-related factors using structural equation modeling—A case study of Seoul City.” Accid. Anal. Prev. 112 (Mar): 1–10. https://doi.org/10.1016/j.aap.2017.12.013.
Lee, J., B. Nam, and M. Abdel-Aty. 2015. “Effects of pavement surface conditions on traffic crash severity.” J. Transp. Eng. 141 (10): 04015020. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000785.
Malin, F., I. Norros, and S. e Innamaa. 2019. “Accident risk of road and weather conditions on different road types.” In Accident analysis and prevention, 181–188. Amsterdam, Netherlands: Elsevier.
Oh, J., S. Washington, and D. Lee. 2010. “Property damage crash equivalency factors to solve crash frequency—Severity dilemma: Case study on South Korean rural roads.” Transp. Res. Rec. 2148 (1): 83–92. https://doi.org/10.3141/2148-10.
Pai, C.-W., and W. Saleh. 2008. “Modelling motorcyclist injury severity resulting from sideswipe collisions at T-junctions in the United Kingdom: New insights into the effects of manoeuvres.” Int. J. Crashworthiness 13 (1): 89–98. https://doi.org/10.1080/13588260701731716.
Plainis, S. 2006. “Road traffic casualties: Understanding the night-time death toll.” Inj. Prev. 12 (2): 125–138. https://doi.org/10.1136/ip.2005.011056.
Prati, G., L. Pietrantoni, and F. Fraboni. 2017. “Using data mining techniques to predict the severity of bicycle crashes.” Accid. Anal. Prev. 101 (Apr): 44–54. https://doi.org/10.1016/j.aap.2017.01.008.
Regev, S., J. J. Rolison, and S. Moutari. 2018. “Crash risk by driver age, gender, and time of day using a new exposure methodology.” J. Saf. Res. 66 (Sep): 131–140. https://doi.org/10.1016/j.jsr.2018.07.002.
Sam, E. F., S. Daniels, K. Brijs, T. Brijs, and G. Wets. 2018. “Modelling public bus/minibus transport accident severity in Ghana.” Accid. Anal. Prev. 119 (Oct): 114–121. https://doi.org/10.1016/j.aap.2018.07.008.
Satria, R., and M. e Castro. 2016. “GIS tools for analyzing accidents and road design: A review.” Transp. Res. Procedia 18 (Jun): 242–247. https://doi.org/10.1016/j.trpro.2016.12.033.
Tsubota, T., C. Fernando, T. Yoshii, and H. Shirayanagi. 2018. “Effect of road pavement types and ages on traffic accident risks.” Transp. Res. Procedia 34 (Jan): 211–218. https://doi.org/10.1016/j.trpro.2018.11.034.
Uddin, M., and N. Huynh. 2017. “Truck-involved crashes injury severity analysis for different lighting conditions on rural and urban roadways.” Accid. Anal. Prev. 108 (Nov): 44–55. https://doi.org/10.1016/j.aap.2017.08.009.
Wang, C., M. A. Quddus, and S. G. Ison. 2011. “Predicting accident frequency at their severity levels and its application in site ranking using a two-stage mixed multivariate model.” Accid. Anal. Prev. 43 (6): 1979–1990. https://doi.org/10.1016/j.aap.2011.05.016.
Wolf, E. J., K. M. Harrington, S. L. Clark, and M. W. e Miller. 2013. “Sample size requirements for structural equation models: An evaluation of power, bias, and solution propriety.” Edu. Psychol. Measure. 73 (6): 913–934. https://doi.org/10.1177/0013164413495237.
Yamamoto, T., J. Hashiji, and V. N. Shankar. 2008. “Underreporting in traffic accident data, bias in parameters and the structure of injury severity models.” Accid. Anal. Prev. 40 (4): 1320–1329. https://doi.org/10.1016/j.aap.2007.10.016.
Ye, F., and D. Lord. 2014. “Comparing three commonly used crash severity models on sample size requirements: Multinomial logit, ordered probit and mixed logit models.” In Analytic Methods in Accident Research, 72–85. Amsterdam, Netherlands: Elsevier.
Zou, W., X. Wang, and D. Zhang. 2017. “Truck crash severity in New York city: An investigation of the spatial and the time of day effects.” Accid. Anal. Prev. 99 (Feb): 249–261. https://doi.org/10.1016/j.aap.2016.11.024.
Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 146 • Issue 7 • July 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Mar 13, 2019
Accepted: Jan 13, 2020
Published online: May 13, 2020
Published in print: Jul 1, 2020
Discussion open until: Oct 13, 2020
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