Traffic Inj Prev. 2026 Mar 11:1-10. doi: 10.1080/15389588.2026.2629613. Online ahead of print.
ABSTRACT
OBJECTIVE: With the continuous improvement of transportation infrastructure, tunnels, as an important type of road connecting key traffic nodes, have become increasingly prominent in the transportation system, and traffic accidents are frequent with serious consequences. Therefore, this study explores the key causal factors of tunnel exit safety traffic accident risk from multiple dimensions, including human factors, traffic conditions, environmental factors, road characteristics, and safety facilities, in order to prevent traffic injuries.
METHODS: Based on the analysis of 851 academic literature research since 2000, an improved Apriori algorithm combining subjective and objective methods was used to calculate support, confidence, and lift to mine frequent itemsets and strong association rules. By using the distance function method to fuze and modify the subjective and objective weights, an indicator system consisting of 5 primary indicators and 14 secondary indicators was constructed. A judgment matrix was constructed using expert questionnaires and AHP data to achieve accurate identification and importance ranking of various causal factors of traffic accident risk.
RESULTS: In the current field of tunnel traffic accidents and safety research, human factors account for 28.24% of the total, becoming the primary focus of attention; traffic safety facilities and road factors follow closely behind with a proportion of 20.21% and 19.47%, respectively, with a total proportion of over 65%, highlighting their core position in tunnel safety research. It is worth noting that 52.94% of scholars focus their research on traffic safety facilities when exploring strategies to improve tunnel traffic safety. The correlation between the color, location, and frequency of traffic safety facilities shows a high degree of causality, with a confidence interval of 0.5526 ∼ 1 and a maximum lift reaching 5.2308. By improving the Apriori algorithm, the key influencing factors for tunnel exit safety are more accurately identified as the location, frequency, and vehicle speed of traffic safety facilities, with weights of 0.2607, 0.2241, and 0.1840, respectively.
CONCLUSIONS: Research findings on key influencing factors of tunnel traffic safety revealed that driver-related factors dominated accident causation, followed by traffic facilities and road factors. Statistical analysis demonstrated significant associations among traffic facility parameters (position, frequency, and color). The improved algorithm quantitatively identified facility position, arrangement frequency, and facility color as critical factors influencing tunnel exit safety. These results provided scientifically validated identification of key determinants, establishing an evidence base for tunnel traffic injury prevention and safety enhancement measures.
PMID:41811364 | DOI:10.1080/15389588.2026.2629613
