A multilevel generalized ordered probit fractional split model for analyzing vehicle speed. (March 2019)

• Record Type: Journal Article
• Authors: Bhowmik, Tanmoy; Yasmin, Shamsunnahar; Eluru, Naveen
• Is Part Of: Analytic methods in accident research. Volume 21(2019)
• Page Start: 13
• Page End: 31
• Publication Date: 2019-03
• Subjects: Vehicle speed -- Arterial road -- Panel mixed generalized ordered probit fractional split model -- Quasi-loglikelihood -- Unobserved factors -- Parameter heterogeneity; Accidents -- Research -- Methodology -- Periodicals; Accidents -- Prevention -- Periodicals; 363.100721
• DOI: 10.1016/j.amar.2018.12.001
• Languages: English
• ISSNs: 2213-6657

Abstract

Highlights: Propose and estimate a panel mixed generalized ordered probit fractional split model. The model allows for the exogenous variable impacts to vary across the alternatives. The model allows for the impact of common unobserved factors across multiple levels. Model is estimated using vehicular speed data from 8 arterial roads in Orlando for the year 2016. Abstract: Vehicle operating speed plays a significant role in many fields of transportation engineering including safety, operation, design and management. The current research effort contributes to literature on examining vehicle speed on arterial roads methodologically and empirically. Specifically, we propose and estimate a panel mixed generalized ordered probit fractional split (PMGOPFS) model to examine critical factors contributing to vehicle operating speed on roadways. The proposed modeling framework allows for the exogenous variable impacts to vary across the alternatives. Further, the model is formulated to allow for the impact of common unobserved factors across multiple levels (roadway, segment, direction, day and time period). To the best of the authors' knowledge, this is the first time such an econometric model is proposed and estimated in any literature (not just in transportation). The proposed model is estimated employing a maximum simulated quasi-likelihood based objective function. Vehicular speed data obtained from 8 arterial roads in Orlando for the year 2016 is used for estimating the model. The data is obtained for weekday morning and evening peak and off-peak hours for one randomly chosen week for each roadway throughout the year. The exogenous variables that are considered in the current empirical study include geometry, roadway, traffic, land use and environmental attributes. The model estimation results are further augmented by conducting elasticity analysis to highlight the important factors affecting the vehicular speed profile.