Analysis of crash proportion by vehicle type at traffic analysis zone level: A mixed fractional split multinomial logit modeling approach with spatial effects. (February 2018)

• Record Type: Journal Article
• Authors: Lee, Jaeyoung; Yasmin, Shamsunnahar; Eluru, Naveen; Abdel-Aty, Mohamed; Cai, Qing
• Is Part Of: Accident analysis and prevention. Volume 111(2018)
• Page Start: 12
• Page End: 22
• Publication Date: 2018-02
• Subjects: Multinomial logit fractional split model -- Traffic crash analysis -- Macroscopic crash analysis -- Traffic analysis zones -- Vehicle type -- Screening; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2017.11.017
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: We proposed an alternative approach to modeling multiple crash count dependent variables. A mixed multinomial logit fractional split model is employed for analyzing the proportions of crashes by vehicle types at the macro-level. The results illustrate the applicability of the proposed approach. A screening method using the proposed model is also suggested. The screening results revealed that the spatial pattern of hot zones is substantially different across the various vehicle types. Abstract: In traffic safety literature, crash frequency variables are analyzed using univariate count models or multivariate count models. In this study, we propose an alternative approach to modeling multiple crash frequency dependent variables. Instead of modeling the frequency of crashes we propose to analyze the proportion of crashes by vehicle type. A flexible mixed multinomial logit fractional split model is employed for analyzing the proportions of crashes by vehicle type at the macro-level. In this model, the proportion allocated to an alternative is probabilistically determined based on the alternative propensity as well as the propensity of all other alternatives. Thus, exogenous variables directly affect all alternatives. The approach is well suited to accommodate for large number of alternatives without a sizable increase in computational burden. The model was estimated using crash data at Traffic Analysis Zone (TAZ) level from Florida. The modeling results clearly illustrate the applicability of the proposed framework for crash proportion analysis. Further, the Excess Predicted Proportion (EPP)—a screening performance measure analogous to Highway Safety Manual (HSM), Excess Predicted Average Crash Frequency is proposed for hot zone identification. Using EPP, a statewide screening exercise by the various vehicle types considered in our analysis was undertaken. The screening results revealed that the spatial pattern of hot zones is substantially different across the various vehicle types considered.