(1) Background: COVID-19 is still affecting people’s daily lives. In the past two years of epidemic control, a traffic control policy has been an important way to block the spread of the epidemic. (2) Objectives: To delve into the blocking effects of different traffic control policies on COVID-19 transmission. (3) Methods: Based on the classical SIR model, this paper designs and improves the coefficient of the infectious rate, and it builds a quantitative SEIR model that considers the infectivity of the exposed for traffic control policies. Taking Changsha, a typical city of epidemic prevention and control, as a study case, this paper simulates the epidemic trends under three traffic control policies adopted in Changsha: home quarantine, road traffic control, and public transport suspension. Meanwhile, to explore the time sensitivity of all traffic control policies, this paper sets four distinct scenarios where the traffic control policies were implemented at the first medical case, delayed by 3, 5, and 7 days, respectively. (4) Results: The implementation of the traffic control policies has decreased the peak value of the population of the infective in Changsha by 66.03%, and it has delayed the peak period by 58 days; with the home-quarantine policy, the road traffic control policy, and the public transport suspension policy decreasing the peak value of the population of the infective by 56.81%, 39.72%, and 45.31% and delaying the peak period by 31, 18, and 21 days, respectively; in the four scenarios where the traffic control policies had been implemented at the first medical case, delayed by 3, 5, and 7 days, respectively, the variations of both the peak value and the peak period timespan of confirmed cases under the home-quarantine policy would have been greater than under the road traffic control and the public transport suspension policies. (5) Conclusions: The implementation of traffic control policies is significantly effective in blocking the epidemic across the city of Changsha. The home-quarantine policy has the highest time sensitivity: the earlier this policy is implemented, the more significant its blocking effect on the spread of the epidemic.

China has actively promoted electronic toll collection (ETC), increasing the proportion of ETC vehicles, and the number of ETC lanes at mainline toll stations has exceeded that of manual toll collection (MTC) lanes. To investigate the effects of ETC and MTC vehicles as guidance objects on the lane-changing behaviors of drivers, we designed three guidance signal plans, including the original sign plan (OR), a complete MTC sign plan (CMS), and a complete MTC sign plan with voice warnings (VW&CMS), for expressway mainline toll lanes. A driving simulator experiment with 40 participants was conducted to evaluate the efficacy of the plans. Generalized estimating equations were used to analyze the characteristics of lane-changing behaviors in different guidance plans, and an entropy weight model using the technique of ranking the order of preference by its similarity to the ideal solution (TOPSIS) was constructed to evaluate the guidance effects of different plans. The results showed that the CMS and VW&CMS plans significantly improved lane-changing behaviors. This improvement is demonstrated by a higher lane-changing ratio, shorter response time, earlier initiation of lane-changing location, higher speed, lower deceleration rate, and longer lane-changing duration distance. These findings can help expressway designers to optimize the guidance-sign system for mainline toll stations.

Public bicycle systems (PBSs) serve as the ‘last mile’ of public transportation for urban residents, yet the problem of the difficulty in borrowing and returning bicycles during peak hours remains a major bottleneck restricting the intelligent and efficient operation of public bicycles. Previous studies have proposed reasonable models and efficient algorithms for optimizing public bicycle scheduling, but there is still a lack of consideration for actual road network distances between stations and the temporal characteristics of demand at rental points in the model construction process. Therefore, this paper aims to construct a public bicycle dispatch framework based on the spatiotemporal characteristics of borrowing and returning demands. Firstly, the spatiotemporal distribution characteristics of borrowing and returning demands for public bicycles are explored, the origin–destination (OD) correlation coefficients are defined, and the intensity of connections between rental point areas is analyzed. Secondly, based on the temporal characteristics of rental point demands, a random forest prediction model is constructed with weather factors, time characteristics, and rental point locations as feature variables, and station bicycle-borrowing and -returning demands as the target variable. Finally, bicycle dispatch regions are delineated based on actual path distances between stations and OD correlation coefficients, and a public bicycle regional dispatch optimization method is established. Taking the PBS in Ningbo City as an example, the balancing optimization framework proposed in this paper is validated. The results show that the regional dispatch optimization method proposed in this paper can achieve optimized dispatch of public bicycles during peak hours. Additionally, compared with the Taboo search algorithm (TSA), the genetic algorithm (GA) exhibits a 11.1% reduction in rebalancing time and a 40.4% reduction in trip cost.

The emergency stop area at expressways is designed to help drivers handle emergencies. However, to enter the emergency stop area, some risky lane-changing behaviors often occur. This study evaluated drivers' lane-changing behavior under various sign schemes to identify an effective sign scheme that can improve lane-changing performance when entering the emergency stop area. Based on the pre-position distance, disappearance distance of the signs, as well as the driver's judgment distance and action distance, different sign positions and number conditions were set. A driving simulator was used to examine drivers' lane-changing behavior when approaching the emergency stop area under various sign schemes. The lane-changing behavior was divided into the decision process and the action process. The Generalized Estimating Equations (GEE) model was used to analyze five selected behavioral variables. Besides, the entropy-weighted TOPSIS model was employed to select the recommended sign scheme. The results show that both the Single position improved (SPI) sign and the Two-stage improved (TSI) sign effectively advanced the drivers' lane-changing position by over 52.4%, increasing the lane-change success rate by more than 23.8% compared to the baseline sign. The TSI sign further increased the lane-changing speed and improved comfort compared to the SPI sign. The entropy-weighted TOPSIS model results identified TSI380 as the recommended sign scheme. Additionally, mature drivers performed better in lane-changing performance than novice drivers, and female drivers were more likely to follow the sign information. The findings provide scientific guidance for the placement of signs before emergency stop areas. It is recommended to set signs at 380 m and 230 m before the entrance of the emergency stop area to reduce risky lane-changing behavior.