Crash Frequency Analysis Using Hurdle Models with Random Effects Considering Short-Term Panel Data
- PMID: 27792209
- PMCID: PMC5129253
- DOI: 10.3390/ijerph13111043
Crash Frequency Analysis Using Hurdle Models with Random Effects Considering Short-Term Panel Data
Abstract
Random effect panel data hurdle models are established to research the daily crash frequency on a mountainous section of highway I-70 in Colorado. Road Weather Information System (RWIS) real-time traffic and weather and road surface conditions are merged into the models incorporating road characteristics. The random effect hurdle negative binomial (REHNB) model is developed to study the daily crash frequency along with three other competing models. The proposed model considers the serial correlation of observations, the unbalanced panel-data structure, and dominating zeroes. Based on several statistical tests, the REHNB model is identified as the most appropriate one among four candidate models for a typical mountainous highway. The results show that: (1) the presence of over-dispersion in the short-term crash frequency data is due to both excess zeros and unobserved heterogeneity in the crash data; and (2) the REHNB model is suitable for this type of data. Moreover, time-varying variables including weather conditions, road surface conditions and traffic conditions are found to play importation roles in crash frequency. Besides the methodological advancements, the proposed technology bears great potential for engineering applications to develop short-term crash frequency models by utilizing detailed data from field monitoring data such as RWIS, which is becoming more accessible around the world.
Keywords: daily crash frequency; hurdle negative binomial; panel data; random effect; short-term driving environment.
Conflict of interest statement
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
References
-
- Lord D., Mannering F. The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives. Transp. Res. A Policy Pract. 2010;44:291–305. doi: 10.1016/j.tra.2010.02.001. - DOI
-
- Mannering F.L., Bhat C.R. Analytic methods in accident research: Methodological frontier and future directions. Anal. Methods Accid. Res. 2014;1:1–22. doi: 10.1016/j.amar.2013.09.001. - DOI
-
- Washington S.P., Karlaftis M.G., Mannering F.L. Statistical and Econometric Method for Transportation Data Analysis. Chapman & Hall/CRC; Boca Raton, FL, USA: 2011.
-
- Zamzuri Z.H.B. Ph.D. Thesis. Macquarie University; Sydney, Australia: Unpublished, 2013. Spatio-Temporal Traffic Accident Modelling.
-
- Lee C., Hellinga B., Saccomanno F. Real-Time Crash Prediction Model for Application to Crash Prevention in Freeway Traffic. Transp. Res. Rec. J. Transp. Res. Board. 2003;1840:67–77. doi: 10.3141/1840-08. - DOI
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