Version 1
: Received: 11 November 2018 / Approved: 13 November 2018 / Online: 13 November 2018 (13:02:16 CET)
How to cite:
Ouni, F.; Belloumi, M. Modeling Traffic Accident Occurrence at Hazardous Road Locations: A Case Study in Tunisia. Preprints2018, 2018110315. https://doi.org/10.20944/preprints201811.0315.v1
Ouni, F.; Belloumi, M. Modeling Traffic Accident Occurrence at Hazardous Road Locations: A Case Study in Tunisia. Preprints 2018, 2018110315. https://doi.org/10.20944/preprints201811.0315.v1
Ouni, F.; Belloumi, M. Modeling Traffic Accident Occurrence at Hazardous Road Locations: A Case Study in Tunisia. Preprints2018, 2018110315. https://doi.org/10.20944/preprints201811.0315.v1
APA Style
Ouni, F., & Belloumi, M. (2018). Modeling Traffic Accident Occurrence at Hazardous Road Locations: A Case Study in Tunisia. Preprints. https://doi.org/10.20944/preprints201811.0315.v1
Chicago/Turabian Style
Ouni, F. and Mounir Belloumi. 2018 "Modeling Traffic Accident Occurrence at Hazardous Road Locations: A Case Study in Tunisia" Preprints. https://doi.org/10.20944/preprints201811.0315.v1
Abstract
The purpose of the present study is to explore the linkage between Hazardous Road Locations-based crash counts and a variety of geometric characteristics, roadway characteristics, traffic flow characteristics and spatial features in the region of Sousse, Tunisia. For this purpose, collision data was collected from at 52 hazardous road sections including 1397 crash records for a 11-year monitoring period from January 1, 2004 to December 31, 2014 obtained from National Observatory for Information, Training, Documentation and Studies on Road Safety in Tunisia (NOITDRS). The matrix of Pearson correlation was used in order to avoid inclusion of both variables, which were highly correlated. Both the Random Effects Negative Binomial model and the Negative Binomial model were estimated. To evaluate the models, the Random Effect Negative Binomial model improves the goodness-of-fit compared to the Negative Binomial model. Average Daily Traffic volume, Curved alignment, Presence of public lighting, Visibility, Number of lane, Presence of vertical/horizontal sign, Presence of rural segment, Presence of drainage system, Roadway surface condition, Presence of paved shoulder and presence of major road were found as significant variables influencing accident occurrences. Overall, the current research contributes to the literature from empirical, modeling methodological standpoints since it was the first study conducted in Tunisia to use crash prediction models for hazardous road locations, and that portrays Tunisian reality. The research findings present advantageous insights on hazardous road locations in the region of Sousse, Tunisia and present useful planning tools for public authorities in Tunisia.
Keywords
Crash counts; Hazardous Road Locations; Pearson correlation; Random effect negative binomial; Negative binomial model
Subject
Business, Economics and Management, Economics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
