Currently, research on road traffic safety is mostly focused on traffic safety evaluations based on statistical indices for accidents. There is still a need for in-depth investigation on preaccident identification of safety risks. In this study, the correlations between high-incidence locations for aberrant driving behaviors and locations of road traffic accidents are analyzed based on vehicle OBD data. A road traffic safety risk estimation index system with road traffic safety entropy (RTSE) as the primary index and rapid acceleration frequency, rapid deceleration frequency, rapid turning frequency, speeding frequency, and high-speed neutral coasting frequency as secondary indices is established. A calculation method of RTSE is proposed based on an improved entropy weight method. This method involves three aspects, namely, optimization of the base of the logarithm, processing of zero-value secondary indices, and piecewise calculation of the weight of each index. Additionally, a safety risk level determination method based on two-step clustering (density and k-means clustering) is also proposed, which prevents isolated data points from affecting safety risk classification. A risk classification threshold calculation method is formulated based on k-mean clustering. The results show that high-incidence locations for aberrant driving behaviors are consistent with the locations of traffic accidents. The proposed methods are validated through a case study on four roads in Chongqing with a total length of approximately 38 km. The results show that the road traffic safety trends characterized by road safety entropy and traffic accidents are consistent.

Bus lanes are an important measure for improving the quality of bus service and the efficiency of transportation systems. A scientific and reasonable evaluation of the overall traffic operation efficiency of the bus priority road section is helpful to fully understand the improvement effect of the introduction of bus lanes on traffic operation. To comprehensively and objectively evaluate the traffic benefits of bus lanes, the Delphi and grey correlation methods were used to construct a comprehensive weight calculation model of the indicators. The weights of eight traffic benefit evaluation indicators at the two levels of buses and general traffic were calculated, and the weights were then optimized using the target optimization model. Combined with different weight indexes, the evaluation of the traffic benefit level of the bus lane was realized using the matter-element extension model based on the improvement in the sticking progress. The bus lanes of the Daping-Yangjiaping, Huanghuayuan interchange-Luneng turntable, and Dashiba-Hongqihegou routes in the main urban area of Chongqing were used for verification. The results show that the traffic benefits of the three case areas have been improved to a certain extent after the construction of bus lanes, but the benefit level has not changed. Through the analysis of various operating indicators, the weaknesses that affect the traffic efficiency can be obtained, and then the decision-making basis for the implementation and improvement of the bus lane optimization scheme can be provided.

Urban tunnels often easily become traffic bottlenecks. Once traffic accidents occur, traffic congestion, environmental pollution, personnel and property damage and other problems restrict the sustainable development of tunnels. In order to reveal the factors affecting the incidence of tunnel traffic accidents, this paper quantitatively analyzes the influence of the single factors of tunnel geometric conditions and control measures on traffic accidents in Jiaozhou Bay underwater tunnel. The study examines the distribution patterns of tunnel traffic accidents under three dual-factor combinations: road gradient and curve radius, road gradient and slope length, and road gradient and the proportion of distance to the bottom of the slope. Based on this, a comprehensive index model is constructed using a negative binomial regression model to calculate the accident occurrence rate in Jiaozhou Bay underwater tunnel under road geometric conditions and control measures. The accident data after the second lining of Jiaozhou Bay underwater tunnel are selected as the validation object. The actual accident occurrence rate is compared with the model’s calculated value to verify the feasibility of the model constructed in this study. The results indicate that high gradient, long slope length, proximity to the bottom of the slope, and straight downhill sections have a significant impact on the occurrence of traffic accidents. Lane change signs can effectively reduce the accident rate by 30–40%. The percentage errors of the left- and right-lane traffic accident prediction models are within (−30%, 30%) and (−20%, 30%), respectively, which can assist in the design and control of underwater tunnels, offering valuable guidance and contributions to the construction of safer, more efficient, and more sustainable urban transportation systems.

S‐curves are common and complex curves in road networks, consisting of two curves in opposite directions connected by a short segment. Drivers’ behavior on S‐curves reflects their ability to navigate complex environments, which is not yet fully understood. This study examines speed patterns on five S‐curves using a naturalistic driving dataset of 2676 vehicle trajectories. A hierarchical clustering method is proposed to identify typical speed patterns on S‐curves, and a mixed logit model is developed to analyze the influence of curve radius, slope, and lighting conditions on drivers’ speed pattern choices. The empirical findings reveal that (1) each S‐curve has dominant speed patterns, and these patterns vary across different S‐curves; (2) speed patterns generally exhibit three types of shapes: S‐shaped, U‐shaped, and anti‐S‐shaped; and (3) the curve radius, the difference in radii between the two adjacent curves, the slope, and lighting conditions significantly influence drivers’ speed pattern choices. These results provide valuable insights for S‐curve alignment design, speed guidance strategies, and speed planning algorithms for autonomous vehicles.

To accurately prevent and warn of traffic accidents, this article proposes a method for predicting urban road traffic safety risks based on vehicle driving behaviour data and information entropy theory. This method uses data from radar video-integrated sensors to calibrate the thresholds for identifying unsafe driving behaviour, introduces recognition principles and algorithms, and analyses spatiotemporal distribution patterns. By incorporating entropy theory, an evaluation system with traffic safety entropy as the primary indicator and the unsafe driving behaviour rate as the secondary indicator is established. Clustering algorithms determine the classification number and threshold of traffic safety entropy, constructing a tunnel traffic safety risk assessment model, which is validated with road accident data. Using 13 days of data from the left lane of Qingdao Jiaozhou Bay Tunnel, the model divides traffic operation risk into high and low categories based on K-means clustering results of accident and safety entropy data. The study finds that when the safety entropy classification threshold is 0.0507, the classification accuracy is the highest at 92%. These results provide technical support for identifying road traffic safety risk points and preventing accidents.

The driving situation is complicated in an extra-long tunnel. If a traffic collision happens, car evacuation and people rescue in the tunnel will be more challenging. The driving safety and traffic efficiency of an extra-long tunnel will be considerably enhanced if vehicle trajectory can be precisely assessed and the vehicle driving risk can be predicted in advance. However, due to the limitations of the data capture of vehicle-mounted equipment in tunnels, estimating vehicle trajectory with vehicle-side data is difficult. In this research, a vehicle-trajectory prediction model based on section traffic data in an extra-long tunnel is developed using existing roadside traffic-gathering equipment. The model re-optimizes the driver-sensitivity coefficient by calibrating the parameters of the vehicle-following model based on mining the motion laws of cars under different scenarios. The model is validated using observed velocity and position data, and the average accuracy of velocity prediction is 94.14% and 95.45% for trajectory prediction. The results show that using existing roadside collecting equipment, this model can accurately forecast vehicle trajectories in extra-long tunnels.