The friction coefficient between the tire and the road is one of the key parameters affecting road traffic safety. The purpose of this paper is to quantify the risk of skidding for the vehicles due to the friction evolution caused by the traffic polishing in the horizontal curve. Based on the reliability theory, an innovative dynamic risk assessment model is developed in the present study for passenger cars and trucks. The influence of two traffic characteristics for pavement friction is quantified: cumulative traffic volume (CTV) and annual average daily traffic of trucks (AADTT). The speed distribution on the horizontal curve of the motorway is obtained through field experiments as the basic parameter of the model. The Hasofer-Lind Method is adopted to solve the reliability and the risk probability of vehicle skidding. The results show that in the traffic characteristics, the AADTT has a significant impact on the pavement friction; When the AADTT on the road exceeds 2000 veh/d, the increasing CTV leads to friction decrease rapidly and therefore has a significant impact on the risk of horizontal curve. Especially for roads with more than 50 million vehicles of the CTV, the risk of the horizontal curve shows a sharp increase with CTV rising. The research results can provide reference for the road maintenance department to determine the timing of road maintenance.

The friction coefficient is one of the dominant parameters affecting vehicle driving stability on horizontal curves. However, there is no comprehensive framework to assess the traffic safety on the horizontal curve with the evolution of the friction coefficient caused by the traffic flow. In light of this, this paper developed an integrated risk-assessment framework to evaluate the safety on the horizontal curve with the friction coefficient evolving under different traffic characteristics. The speed distribution on the horizontal curve of the freeway is obtained through field experiments that serve as the basic parameters of the model. A new multi-vehicle risk index (MRI) is introduced to assess the traffic safety risk for the horizontal curve by coupling the reliability theory and negative binomial. Three traffic characteristics are considered in the analysis: cumulative traffic volume (CTV), annual average daily traffic (AADT), and average daily traffic of heavy goods vehicles (AADT_HGV). The results show that the AADT and AADT_HGV have a considerable impact on the road risk level. When the truck traffic volume is less than 1000 veh/d, the risk of horizontal curves changes less as road operational time goes. The research results can provide a reference for the road maintenance department to determine the timing of road maintenance.

Sparse road networks in high-risk geological disaster areas, characterized by long segments, few nodes, and limited alternative routes, face significant vulnerabilities to geological hazards such as landslides, rockfalls, and collapses. These disruptions hinder emergency response and resource delivery, highlighting the need for enhanced resilience strategies. This study develops a dynamic resilience assessment framework using a two-layer topological model to analyze and optimize the resilience of such networks. The model incorporates trunk and local layers to capture dynamic changes during disasters, and it is validated using the road network in Tibet. The findings demonstrate that critical nodes, including tunnels, bridges, and interchanges, play a decisive role in maintaining network performance. Resilience is influenced by disaster type, duration, and traffic capacity, with collapse events showing moderate resilience and debris flows exhibiting rapid recovery but low survivability. Notably, half-width traffic interruptions achieve the highest overall resilience (0.7294), emphasizing the importance of partial traffic restoration. This study concludes that protecting critical nodes, optimizing resource allocation, and implementing adaptive management strategies are essential for mitigating disaster impacts and enhancing recovery. The proposed framework offers a practical tool for decision-makers to improve transportation resilience in high-risk geological disaster areas.