Sensitivity Analysis and Traffic Safety Risk Assessment of Expressways Using AHP-TOPSIS-RSR.
To reduce and prevent traffic accidents, it is essential to transition from a reactive approach of "punishing after the fact to proactive preventive measures. This paper presents a traffic safety risk evaluation system for expressways, integrating 16 factors such as steering performance and road conditions. A model for assessing traffic safety risk on expressways is proposed using a combination of the AHP-TOPSIS-RSR approach. The AHP method helps form the evaluation matrix, while the TOPSIS method, incorporating entropy, is used to determine the relative closeness between the overall traffic safety risk index and ideal values. These values are then categorized based on expressway risk grades using a hierarchical standard. Additionally, a sensitivity analysis model is introduced through an orthogonal test to compute the sensitivity values of each factor and rank the most influential factors. The proposed method is validated through the Yongwu Expressway. The findings show that the safety risk of the Yongwu Expressway is rated as Level I, indicating a high-risk situation. The sensitivity values for factors like fatigue/unlicensed driving, vehicle speed, road layout, traffic flow, adverse weather conditions, and emergency response systems are 0.4721, 0.5088, 0.7598, 0.8142, 0.5296, and 0.4685, respectively, marking them as the most sensitive risk factors. The risk assessment accurately mirrors the traffic safety conditions of the Yongwu Expressway.
Authors
- Otoka Niabanga Dania Madeleina
- B. Landry
- Diaby Zakarya
- Zhu Honglan
- Rumbidzai Tendai Nyakujara
- Josepha Fansi Nguietchuan
- Ndotoni Madly Eloim Rebecca
- Ntumba Israel Mbala
Publication Details
- Journal
- Academic Journal of Science and Technology
- Published
- 2025-04-21
- DOI
- https://doi.org/10.54097/tddpch29
- Citations
- 1
- Primary Topic
- Risk and Safety Analysis
- Type
- article
- Field-Weighted Citation Impact
- 1.40
Funders
- Huaiyin Institute of Technology