Abstract:

A risk assessment method that combines risk probability and risk loss has been established to address the problem of insufficient consideration of driver status and risk loss in intersection risk assessment. The entropy weight method is utilized to determine the risk probability based on drivers' physiological information， while direct risk loss is calculated using the energy conversion theorem， and indirect risk loss is identified through the introduction of environmental vulnerability metrics. To prioritize intersection risk， a clustering algorithm is applied to rank both risk probability and risk loss. Subsequently， the intersection risk rank is determined through a risk matrix analysis. Empirical using data from 19 intersections in Xi'an city demonstrates that intersection risk levels can be categorized into four tiers： relatively safe， generally safe， relatively risky， and very risky， with a predominant concentration in the relatively risky category. The proposed intersection risk assessment method based on drivers' physiological information matches the perceptions of test drivers with 90% accuracy， providing a new method for driver risk management.

Key words: transportation planning and management, physiological indicators, urban intersections, risk assessment, entropy weight method, K-means clustering, risk matrix