Freeway Accident Likelihood Prediction Using a Panel Data Analysis Approach
Publication: Journal of Transportation Engineering
Volume 133, Issue 3
Abstract
The ability to predict freeway accident likelihood promises significant benefits to freeway operations. However, the development of such prediction models has proven to be very challenging because of the random nature of accidents, as well as the impact of site-specific factors. In addition, accident data has a pronounced nature of discrete response—a preponderant portion of nonaccident cases. To address these challenges, this research investigates the use of a discrete response model designed for panel data—the random effects ordered probit model, in predicting freeway accident likelihood. Panel data refers to data sets that combine time series and cross section (i.e., from different individuals, groups, etc.) observations. The empirical results of this research illustrate that the random effects ordered probit model performs well in identifying factors associated with traffic accidents. In addition, when applied in a predictive setting, the model provides benefits in forecasting the likelihood of accidents based on both time-varying and site-specific parameters.
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Acknowledgments
The writers wish to extend their thanks and appreciation to the anonymous referees and editor whose constructive suggestions greatly improved this work. Nevertheless, any remaining errors and omissions remain the responsibility of the writers.
References
Abdel-Aty, M., Uddin, N., Pande, A., Abdalla, M. F., and Hsia, L. (2004). “Predicting freeway crashes based on loop detector data using matched case-control logistic regression.” Transportation Research Record. 1897, Transportation Research Board, Washington, D.C., 88–95.
Andreescu, M., and Frost, D. (1998). “Weather and traffic accidents in Montreal.” Canada Climate Research, 9, 225–230.
Bhat, C. (1999). “An analysis of evening commute stop-making behavior using repeated choice observations from a multi-day survey.” Transp. Res., Part B: Methodol., 33, 495–510.
Danielis, R., Marcucci, E., and Rotaris, L. (2005). “Logistics managers’ stated preferences for freight service attributes.” Transp. Res. Part E, 41, 201–215.
Fridstrøm, L., Ifver, J., Ingebrigtsen, S., Kulmala, R., and Thomsen, L. (1995). “Measuring the contribution of randomness, exposure, weather, and daylight to the variation in road accident counts.” Accid. Anal Prev., 27(1), 1–20.
Garber, N. J., and Subramanyan, S. (2001). “Incorporating crash risk in selecting congestion-mitigation strategies: Hampton Roads area (Virginia) case study.” Transportation Research Record. 1746, Transportation Research Board, Washington, D.C., 1–5.
Greene, W. H. (2000). Econometric analysis, Prentice-Hall, Englewood Cliffs, N.J.
Lee, C., Hellinga, B., and Saccomanno, F. (2003). “Real-time crash prediction model for the application to crash prevention in freeway traffic.” Transportation Research Record. 1840, Transportation Research Board, Washington, D.C., 67–77.
Lee, C., Saccomanno, F., and Hellinga, B. (2002). “Analysis of crash precursors on instrumented freeways.” Transportation Research Record. 1784, Transportation Research Board, Washington, D.C., 1–8.
Lee, J., and Mannering, F. (2002). “Impact of roadside features on the frequency and severity of run-off-roadway accidents: An empirical analysis.” Accid. Anal Prev., 34, 149–161.
Lord, D., Washington, S. P., and Ivan, J. N. (2004). “Statistical challenges with modeling motor vehicle crashes: Understanding the implications of alternative approaches.” 83rd Annual Meeting of Transportation Research Board, Washington, D.C.
Noland, R. B. (2003). “Traffic fatalities and injuries: the effect of changes in infrastructure and other trends.” Accid. Anal Prev., 35, 599–611.
Oh, C., Oh, J., Ritchie, S., and Chang, M. (2001). “Real-time estimation of freeway accident likelihood.” Presented at the 80th Annual Meeting of Transportation Research Board, Washington, D.C.
Persaud, B. N., and Dzbik, L. (1993). “Accident prediction models for freeways.” Transportation Research Record. 1401, Transportation Research Board, Washington, D.C., 55–60.
Shafizadeh, K., and Mannering, F. (2006). “Statistical modeling of user perceptions of infrastructure condition: Application to the case of highway roughness.” J. Transp. Eng., 132(2), 133–140.
Shankar, V., Albin, R., Milton, J., and Mannering, F. (1998). “Evaluating median crossover likelihoods with clustered accident counts: An empirical inquiry using the random effects negative binomial model.” Transportation Research Record. 1635, Transportation Research Board, Washington, D.C., 44–48.
Shankar, V., Mannering, F., and Barfield, W. (1995). “Effect of roadway geometrics and environmental factors on rural freeway accident frequencies.” Accid. Anal Prev., 27(3), 371–389.
Turochy, R. E., and Smith, B. L. (2000). “New procedure for detector data screening in traffic management systems.” Transportation Research Record. 1727, Transportation Research Board, Washington, D.C., 127–131.
Washington, S. P., Karlaftis, M. G., and Mannering, F. (2003). Statistical and econometric methods for transportation data analysis, Chapman and Hall/CRC.
Zhang, C., Ivan, J. N., ElDessouki, W. M., and Anagnostou, E. N. (2005). “Relative risk analysis for studying the impact of adverse weather conditions and congestion on traffic accidents.” 84th Annual Meeting of Transportation Research Board, Washington, D.C.
Zhou, M., and Sisiopiku, V. P. (1997). “Relationship between volume-to-capacity ratios and accident rates.” Transportation Research Record. 1581, Transportation Research Board, Washington, D.C., 47–52.
Information & Authors
Information
Published In
Journal of Transportation Engineering
Volume 133 • Issue 3 • March 2007
Pages: 149 - 156
Copyright
© 2007 ASCE.
History
Received: Feb 24, 2005
Accepted: Jul 26, 2006
Published online: Mar 1, 2007
Published in print: Mar 2007
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