When traffic collisions occur on urban expressways, the consequences, including injuries, the loss of lives, and damage to properties, are more serious. However, the existing research on the severity of expressway traffic collisions has not been deeply explored. The purpose of this research was to investigate how various factors affect the severity of urban expressway collisions. The severity of urban expressway collisions was set as the dependent variable, which could be divided into three categories: slight collisions, severe collisions, and fatal collisions. Ten variables, including individual characteristics, collision characteristics, and road environment conditions, were selected as independent factors. Based on 975 valid urban expressway collisions, an ordered logistic regression model was established to evaluate the impacts of influence factors on the severity of these crashes. The results show that gender, collision modality, road pavement conditions, road surface conditions, and visibility are significant factors that affect the severity of urban expressway collisions. Females were more likely to be involved in more severe urban expressway collisions than males. For collisions involving pedestrians and non-motorized vehicles, the risk of more severe injury was 7.508 times higher than that associated with vehicle–vehicle collisions. The probability of more severe collisions on urban expressways with poor pavement conditions and wet surface conditions is greater than that on urban expressways with good pavement conditions and dry surface conditions. In addition, as visibility increases, the probability of more severe collisions on urban expressways gradually decreases. These results provide more effective strategies to reduce casualties as a result of urban expressway collisions.

Overtaking behavior between non-motorized vehicles is one of the main characteristics of the cycling path, and unsafe overtaking behavior has a certain negative impact on riders’ safety. However, little is known about the factors affecting riders’ overtaking decisions. This study aimed to identify the influence of road facilities, types of non-motorized vehicles, and human factors on the characteristics of overtaking behavior on bicycle lanes. DJI drone-based naturalistic riding research was explored in China and a random parameter logit regression model was estimated to model the overtaking decisions of non-motorized vehicle riders. The results showed that gender, age, professional deliverer, type of lead non-motor vehicle, type of non-motorized vehicles, and width of cycling lane influence overtaking behavior significantly. The present study provides theoretical evidence to strengthen the safety design and evaluation of cycling lane infrastructure.

As a key element of the sharing economy, ride-sharing plays a vital role in promoting sustainable urban mobility by optimizing vehicle utilization rates, lowering carbon emissions, and alleviating traffic congestion. Despite its cost-efficiency and sustainability benefits, ride-sharing adoption remains limited in the post-pandemic period due to behavioral changes and safety concerns. Accordingly, using survey data from 425 commuters in Hefei, concerns about COVID-19 and satisfaction with ride-sharing services were integrated into the theory of planned behavior framework. Structural equation modeling was applied to examine the relationship between ride-sharing intention and actual usage behaviors. The results indicated that ride-sharing intention was significantly positively affected by subjective norms (β = 0.428 ***), service satisfaction (β = 0.315 ***), and perceived behavioral control (β = 0.162 *), but significantly negatively affected by concerns about COVID-19 (β = −0.183 **). Concerns about COVID-19 significantly negatively affected travelers’ actual ride-sharing behaviors (β = −0.2 **). Furthermore, ride-sharing intention was identified as a significant positive predictor of travelers’ behaviors: specifically, their likelihood of accepting a ride-sharing order (β = 0.395 ***). These findings offer transport authorities evidence-based strategies for designing targeted interventions during health crises, particularly through reinforcing social norms, improving service quality, and implementing transparent health protocols to ensure both user safety and sustainability.

The current study investigates the effects of speed and time headway of human-machine co-driving vehicles on pedestrian crossing speed at uncontrolled mid-block road sections. A VR-based simulation study is conducted to study pedestrian crossing behaviour when facing human-machine co-driving vehicles. A total of 30 college students are recruited, and each participant is required to complete 5 street-crossing simulator trials facing human-machine co-driving vehicles with varying time headway levels and speeds. The correlations and differences between demographic information, time headway, vehicle speed, and pedestrian crossing speed are analyzed. The results show that gender and pedestrian’s trust in human-machine co-driving vehicles are significantly correlated with pedestrian crossing speed. The pedestrian crossing speed increases with the increase in vehicle speed and decreases with the increase in vehicle time headway. In addition, the time headway has a stronger correlation with the pedestrian crossing speed than the vehicle speed. The findings will provide theoretical and methodological support for the formulation of pedestrian crossing control measures in the stage of human-machine co-driving.