Crash Frequency Analysis Using Hurdle Models with Random Effects Considering Short-Term Panel Data
AbstractRandom 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. View Full-Text
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Chen, F.; Ma, X.; Chen, S.; Yang, L. Crash Frequency Analysis Using Hurdle Models with Random Effects Considering Short-Term Panel Data. Int. J. Environ. Res. Public Health 2016, 13, 1043.
Chen F, Ma X, Chen S, Yang L. Crash Frequency Analysis Using Hurdle Models with Random Effects Considering Short-Term Panel Data. International Journal of Environmental Research and Public Health. 2016; 13(11):1043.Chicago/Turabian Style
Chen, Feng; Ma, Xiaoxiang; Chen, Suren; Yang, Lin. 2016. "Crash Frequency Analysis Using Hurdle Models with Random Effects Considering Short-Term Panel Data." Int. J. Environ. Res. Public Health 13, no. 11: 1043.
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