Accident severity levels and traffic signs interactions in state roads: a seemingly unrelated regression model in unbalanced panel data approach. (November 2018)

• Record Type: Journal Article
• Authors: Xu, Xuecai; Šarić, Željko; Zhu, Feng; Babić, Dario
• Is Part Of: Accident analysis and prevention. Volume 120(2018)
• Page Start: 122
• Page End: 129
• Publication Date: 2018-11
• Subjects: Accident severity level -- Traffic sign -- Seemingly unrelated regression -- Panel data; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2018.07.037
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: The interactions between accident severity levels and traffic signs are identified in state roads. A seemingly unrelated regression (SUR) model in unbalanced panel data approach is proposed. The seemingly. unrelated regression model addresses the correlation of residuals between different accident severity levels, and the panel data model accommodates the heterogeneity attributed to unobserved factors. By comparing the pooled, fixed-effects and random-effects SUR models, the random-effects SUR model shows priority to the other two. The results reveal that different traffic signs cause different accident severity levels, which benefits the policy makers and roadway management departments. Abstract: This study intended to investigate the interactions between accident severity levels and traffic signs in state roads located in Croatia, and explore the correlation within accident severity levels and heterogeneity attributed to unobserved factors. The data from 410 state roads between 2012 and 2016 were collected from Traffic Accident Database System maintained by the Republic of Croatia Ministry of the Interior. To address the correlation and heterogeneity, a seemingly unrelated regression (SUR) model in unbalanced panel data approach was proposed, in which the seemingly unrelated model addressed the correlation of residuals, while the panel data model accommodated the heterogeneity due to unobserved factors. By comparing the pooled, fixed-effects and random-effects SUR models, the random-effects SUR model showed priority to the other two. Results revealed that (1) low visibility and the number of invalid traffic signs per km increased the accident rate of material damage, death or injured; (2) average speed limit exhibited a high accident rate of death or injured; (3) the number of mandatory signs was more likely to reduce the accident rate of material damage, while the number of warning signs was significant for accident rate of death or injured.