On-board data of detected vehicles play a critical role in the management of urban road traffic operation and the estimation of traffic status. Unfortunately, due to limitations of technology and privacy issues, the sampling frequency of the detected vehicle data is low and the coverage is also limited. Continuous vehicle trajectories cannot be obtained. To overcome the above problems, this paper proposes an unscented Kalman filter (UKF)-based method to reconstruct the trajectories at signalized intersections using sparse probe data of vehicles. We first divide the intersection into multiple road sections and use a quadratic programming problem to estimate the travel time of each section. The weight of each initial possible trajectory is calculated separately, and the trajectory is updated using the unscented Kalman filter (UKF); then, the trajectory between two updates is also obtained accordingly. Finally, the method is applied to the actual scenario provided by the NGSIM data and compared with the real trajectory. The mean absolute error (MAE) is adopted to evaluate the accuracy of the proposed trajectory reconstruction. Sensitivity analysis is provided in order to provide the requirement of sampling frequency to obtain highly accurate reconstructed vehicle trajectories under this method. The results demonstrate the applicability of the technique to the signalized intersection. Therefore, the method enables us to obtain richer and more accurate trajectory data information, providing a strong prior basis for future urban road traffic management and scholars using trajectory data for various studies.

The visibility in a foggy environment has a significant impact on driver behavior and traffic flow status, especially for whole closed highways with long distances between entrances and exits. Foggy days are very likely to cause congestion and even secondary traffic accidents, which seriously affect the reliability of freeway operation. In order to explore the influence of a fog environment on freeway traffic jams, firstly, this paper was based on the analysis of the impact of visibility on foggy days. Light fog, medium fog and heavy fog were classified as one scenario, while dense foggy weather was set separately as an extreme scenario without considering lane change. Furthermore, it used the SIR model of infectious disease for reference, and combined with the cellular automata (CA) model, the car-following model and lane-changing rules in different scenarios were set based on safe driving distance and speed for two scenarios. Finally, the key parameters of CA-SIR were calibrated, such as congestion propagation, recovery probability, vehicle braking, and lane-changing probability. The simulation analysis showed that with the decrease in visibility and vehicle speed, the phenomenon of congestion propagation was more prominent, but the causes of queuing phenomenon were different. A low speed limit was the main reason for traffic jams in the light fog condition. In the medium fog condition, the frequency of traffic jams was related to the random braking probability of the visibility. In heavy fog conditions, the congestion area gradually moved upstream with the passage of time. Moreover, in the dense fog condition, the congested area gradually moved upstream with the passage of time; however, vehicles were more likely to accompany each other, and the congested area traveled downstream synchronously with the passage of time and did not dissipate easily. Therefore, in a foggy environment, the best speed limit should be better established under different visibilities, the flow of highway traffic should be strictly controlled if necessary, and in worse situations than high-density traffic in low visibility, to avoid the spread of congestion, the intermittent release of different lanes is suggested to be implemented.

To enhance the safety of pedestrians crossing the street, a series of new regulations regarding pedestrian yield has been proposed and widely implemented across cities. In this study, we first made some improvements to the social force model, in which pedestrian crossing at the intersection, drivers’ psychology of giving way, vehicle yield to pedestrians, vehicle yield in different directions, the influence of pedestrians crossing boundaries, and signal lamp groups on pedestrian behavior were considered. Furthermore, pedestrian crossing and vehicle yield safety models were established, based on which the comprehensive safety evaluation model of intersections in arterials was established, in which two indices—(1) the safety degree of pedestrian crossings and (2) vehicle acceleration interference—were combined with the entropy weight method. Finally, four types of intersections in arterials were studied using a simulation: the intersections between different levels of arterials, and intersections with one-time and two-times pedestrian crossings. Moreover, safety evaluation and analysis of those intersections, considering the rule of pedestrian yield, were conducted combined with the trajectory data from the VISSIM simulation. The relevant results showed that for pedestrians crossing the street, the pedestrian safety of two-time crossing is significantly higher than that of one-time crossing, and compared with the arterial, the pedestrian crossing distance of the sub-arterial is shorter, and the pedestrian perception is safer. Moreover, due to the herd psychology effect, the increase in pedestrian flow volume improves the safety perception of pedestrians at the intersection.

Oriented to characteristics of the inflow and outflow of routes in urban road network, we modified the classical fundamental green wave bandwidth model, in which separate turning green wave band is available for traffic flow from subarterials merging into an arterial, and this variable green wave band can be more flexible to service the commuting traffic. Moreover, with the analysis of the mapping characteristics of the phase coordination rate, the concept of the coordination rate of green wave bandwidth was proposed, with which as the objective function, a multiroute signal coordination control model was established, and this model is a mixed integer linear programming problem with the overall optimal coordination rate of inbound, outbound, and turning movement as the objective. Finally, a case study was given with road network in Suzhou Industrial Park, Jiangsu Province, China. From the simulation results, we can conclude that the coordinated distribution of the model proposed in this study is more stable; the fluctuation range is 0.09, which is less than that of optimization scheme in classical signal timing software Synchro, which is 0.33; and the total route delay can also be reduced by 15% compared to the current situation and 3.3% compared to Synchro optimization solution.