Freeway Curve Safety Evaluation Based on Truck Traffic Data Extracted by Floating Car Data

Fu’an Lan, Chi Zhang (), Min Zhang, Yichao Xie and Bo Wang
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Fu’an Lan: School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China
Chi Zhang: School of Highway, Chang’an University, Xi’an 710064, China
Min Zhang: School of Transportation Engineering, Chang’an University, Xi’an 710064, China
Yichao Xie: School of Highway, Chang’an University, Xi’an 710064, China
Bo Wang: School of Highway, Chang’an University, Xi’an 710064, China

Sustainability, 2025, vol. 17, issue 9, 1-21

Abstract: Due to complex traffic conditions, freeway curves are associated with higher crash rates, particularly for trucks, which poses significant safety risks. Predicting truck crash rates on curves is essential for enhancing freeway safety. However, geometric design consistency indicators (GDCIs) are limited in terms of their ability to evaluate safety levels. To address this, this study identifies key factors influencing truck crash rates on curves and proposes a new safety evaluation indicator, the mean speed change rate (MSCR). A vague set, as an extension of the fuzzy set, was employed to integrate the MSCR and GDCI to identify high-risk curves. The factors contributing to differences in crash rates between the curves to the left and right are also analyzed. To assess the proposed approach, a case study was conducted using truck traffic data extracted from floating car data (FCD) collected on 32 freeway curves. The results demonstrate that the deflection angle, radius, and deflection direction are key contributions to truck crash risks. Importantly, the recognition accuracy of the MSCR indicator for crash risks on curves to the left and right is improved by 11.8% and 18.2% compared with GDCIs. Combining the proposed MSCR indicator with GDCIs can more comprehensively evaluate the safety of curves, with recognition accuracy rates of 88.2% and 27.3%, respectively. The indicator change value of the curves to the left are always larger, and the difference is more obvious as the geometric indicator changes. The MSCR indicator provides a more comprehensive curve safety assessment method than existing indicators, which is expected to promote the formulation of curve safety management strategies and further achieve sustainable development goals.

Keywords: circular curve safety; floating car data; binary logistic regression; improved safety evaluation indicator; vague sets (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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