To provide a better understanding of spatiotemporal characteristics of vehicle trajectories in connected vehicle environment, a driving simulation study was designed and conducted with an extra-long tunnel scenario. 35 drivers were recruited to participate in the driving experiment. To evaluate the spatiotemporal characteristics of vehicles with and without a warning system, objective measures were analyzed, including a spatiotemporal diagram of the curvature of obtained data and speed adjustment behaviors. This article also evaluated the impacts of connected vehicles on the traffic capacity based on the converging pattern mining method. The results indicated that the in-vehicle human–machine interface (HMI) improved driving behavior and traffic capacity. Notably, the in-vehicle HMI helped drivers better prepare for speed adjustments when approaching the tunnel and when the vehicle in front of the study vehicle made a sudden operational change. Moreover, the system contributed to a more stable operation speed, especially near the tunnel entrance, than that without the system. The findings suggest that connected vehicle environments enable drivers to change from traditional visual stimuli response behaviors to proactive response behaviors based on psychological expectations. Besides, based on the best-converging patterns from the spatiotemporal trajectories of 35 drivers, the results revealed that the traffic capacity could be improved by 22.19% under the experimental traffic flow conditions. Moreover, the differences in the benefits of the in-vehicle HMI among individuals were found to be statistically significant.

An insufficient functional relationship between adjustment factors and saturation flow rate (SFR) in the U.S. Highway Capacity Manual (HCM) method increases an additional prediction bias. The error of SFR predictions can reach 8–10%. To solve this problem, this paper proposes a comprehensive adjusted method that considers the effects of interactions between factors. Based on the data from 35 through lanes in Beijing and 25 shared through and left-turn lanes in Washington, DC, the interactions between lane width and percentage of heavy vehicles and proportion of left-turning vehicles were analyzed. Two comprehensive adjustment factor models were established and tested. The mean absolute percentage error (MAPE) of model 1 (considering the interaction between lane width and percentage of heavy vehicles) was 4.89% smaller than the MAPE of Chinese National Standard method (Standard Number is GB50647) at 13.64%. The MAPE of model 2 (considering the interaction between lane width and proportion of left-turning vehicles was 33.16% smaller than the MAPE of HCM method at 14.56%. This method could improve the accuracy of SFR prediction, provide support for traffic operation measures, alleviate the traffic congestion, and improve sustainable development of cities.

In view of the benefits and a promising prospect of the emerging connected vehicles (CV), more and more vehicles will be equipped with the in‐vehicle human‐machine interface (HMI). This study aims to assess the comprehensive impacts of HMIs on individual driving performance, traffic safety, and eco‐driving behaviour based on a driving simulator. In order to explore how a driver responds to warnings when approaching a tunnel, this study divided the experimental road into five zones according to warning positions. A comparative analysis was conducted to examine the changes in driving comfort, speeding behaviour, the standard deviation of speed, time headway (THW), fuel consumption, and emissions. Results showed that the HMI could be beneficial to speed adjustment before entering a tunnel. Meanwhile, HMI may affect the THW of vehicles. Drivers were usually more cautious when the HMI was on. However, no significant effects of the HMI for driving comfort and eco‐driving behaviour can be observed. It is expected that more efficient technology and different warning strategies can be developed to enhance eco‐safe driving for different zones because they have different effects. The findings of this study are also helpful for active safety management and for evaluating the effectiveness of CV systems.

Intelligent transportation has become a hot research field in recent years The development direction of road traffic construction in the future, the relevant technologies and methods in the process of gradual promotion and application of intelligent connected vehicles continue to attract the attention of scholars and engineers. There are more and more relevant theories, methods and systems. This paper summarizes the current state of microscopic and macroscopic traffic models, characteristic analysis methods of mixed traffic flow, and lane management methods in connected vehicle environments. At the end of this paper, the conclusions of this work are presented, and possible future directions for safety warning research under connected vehicle environments are discussed. This paper represents the current research status of traffic flow characteristics under connected vehicle environments to some extent, which can provide references for future traffic flow characteristic research in terms of framework, methods and technologies, etc.

This paper regards soft rock as a heavily overconsolidated clay and proposes a new fractional elastoplastic model to describe its temperature-dependent mechanical behaviour. Together with the critical state mechanics, the subloading surface concept is adopted to capture the irreversible plastic deformation developed inside the normal yield surface and provides a smooth transition between the elastic and plastic zones. In addition, the proposed model uses a fractional-order flow rule to account for the nonorthogonality between the plastic flow direction and the yield surface without introducing an extra plastic potential. The evolution law of the fractional-order is affected by the degree of overconsolidation and temperature. The proposed model is verified by the drained triaxial test data of Ohya rock under various confining pressures and temperatures with satisfactory performance. It can be found that an increase in the temperature will reduce the peak strength of soft rock and lead to a ductile failure pattern with a smaller tangent modulus.