Objective: This study aimed to identify the traffic flow variables contributing to crash risks under different traffic states and to develop a real-time crash risk model incorporating the varying crash mechanisms across different traffic states. Methods: The crash, traffic, and geometric data were collected on the I-880N freeway in California in 2008 and 2009. This study considered 4 different traffic states in Wu's 4-phase traffic theory. They are free fluid traffic, bunched fluid traffic, bunched congested traffic, and standing congested traffic. Several different statistical methods were used to accomplish the research objective. Results: The preliminary analysis showed that traffic states significantly affected crash likelihood, collision type, and injury severity. Nonlinear canonical correlation analysis (NLCCA) was conducted to identify the underlying phenomena that made certain traffic states more hazardous than others. The results suggested that different traffic states were associated with various collision types and injury severities. The matching of traffic flow characteristics and crash characteristics in NLCCA revealed how traffic states affected traffic safety. The logistic regression analyses showed that the factors contributing to crash risks were quite different across various traffic states. To incorporate the varying crash mechanisms across different traffic states, random parameters logistic regression was used to develop a real-time crash risk model. Bayesian inference based on Markov chain Monte Carlo simulations was used for model estimation. The parameters of traffic flow variables in the model were allowed to vary across different traffic states. Compared with the standard logistic regression model, the proposed model significantly improved the goodness-of-fit and predictive performance. Conclusions: These results can promote a better understanding of the relationship between traffic flow characteristics and crash risks, which is valuable knowledge in the pursuit of improving traffic safety on freeways through the use of dynamic safety management systems.

On urban streets with bus stops, bus arrivals can disrupt traffic flows in the neighboring areas. Different stop designs have distinct influences on the road users. This study aims to evaluate how different types of bus stops affect the operations of vehicles, bicycles, and buses that pass by. Four types of stops that differ in geometric layout are examined. They are termed the shared bike/bus (Type 1), separated shared bike/bus (Type 2), vehicle/bus with inboard bike lane (Type 3), and bus bay with inboard bike lane (Type 4). Data are collected from eight sites in two cities of China. Results of data analysis show that different bus stop designs have quite different impacts on the neighboring traffic flows. More specifically, Type 3 stops create the least bicycle delay but the largest vehicle delay. Type 4 stops have the least impact on bicycle and vehicle operations, but occupy the most road space. Traffic operations are less affected by Type 1 stops than by Type 2 stops. Policy suggestions are discussed regarding the optimal design of bus stops that minimizes the total vehicle delay of all modes.