Modeling crash risk of horizontal curves using large-scale auto-extracted roadway geometry data. (September 2020)

• Record Type: Journal Article
• Authors: Ma, Qingyu; Yang, Hong; Wang, Zhenyu; Xie, Kun; Yang, Di
• Is Part Of: Accident analysis and prevention. Volume 144(2020)
• Publication Date: 2020-09
• Subjects: Horizontal curves -- Safety risk -- Open-source data -- Elevation -- Logistic regression -- Random parameters; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2020.105669
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: Developed a H-curve extraction tool for large-scale data collection. Used random-parameter logistic regression models for H-curve crash analysis. Enhanced H-curve crash analysis with detailed elevation and roadway geometry data. Compared crash risks of H-curves on different types of highways. Abstract: Highway horizontal curves (H-curves) provide a smooth transition between two tangent sections of roadways. They allow vehicles to adjust their travel directions gradually. However, the geometry changes of the highway sections with H-curves often raise safety concerns. In order to deploy effective safety countermeasures, a critical task is to understand the risk factors associated with H-curves. Existing studies have made efforts to probe the safety issues associated with H-curves, whereas they were limited to relatively small-scale examinations because of the challenges in identifying H-curves from large road networks. In addition, due to the lack of well-archived traffic and roadway information, gathering other data associated with the H-curves is also difficult. Regarding to these gaps, this study aims to leverage open-source data to analyze the crash risk of highway sections with H-curves. In particular, the present study highlights itself from two main aspects: (i) a H-curve extraction tool was developed to facilitate large-scale curve data collection through the analytics of different open source data; and (ii) a crash modeling framework was developed to quantify H-curve crash risk. A case study based on a statewide road network was performed to test the developed crash risk models with the collected curve data. The results show the opportunities of using the developed tool for large-scale data collection and analyze the safety impacts of H-curve geometric properties, elevation change, traffic exposure, among others. Findings of this study provide insights into the improvement of H-curve data collection and safety evaluation.