In summer, black asphalt pavement can absorb a considerable amount of solar radiation, which causes its temperature to rise. Heated asphalt pavement can aggravate the urban heat island (UHI) effect and transfer heat downward, which may cause the problem of permafrost thawing beneath pavements. The objective of this study was to develop a novel cool coating layer (CCL) with high near-infrared reflectance and heat insulation to make the surface of asphalt pavement cool. A self-developed test device and method was established to evaluate cooling effects. Based on the experimental results, the optimal coating can cool asphalt pavement by 11.21 °C when the radiation striking the sample surface is 650 W/m². This coating, called the composite cool coating layer (CCCL), is composed of the following materials: polyurethane resin, rutile TiO₂ of 18%, hollow glass microspheres of 12%, and copper chromite black spinel of 0.7%. Silicon carbide particles of 1 kg/m² can help the CCCL achieve satisfactory antiskid performance. In conclusion, CCCL can effectively inhibit the absorption of solar radiation and reduce the flow of thermal energy downward without sacrificing skid resistance.

To improve the effectiveness of model predictive control (MPC) in dynamic traffic signal control strategies, it has been combined with graph convolutional networks (GCNs) and deep reinforcement learning (DRL) technologies. In this study, a neural-network-based traffic signal control optimization method under the MPC framework is proposed. A dynamic correlation matrix is introduced in the predictive model to adapt to the dynamic changes in correlations between nodes over time. The signal control optimization strategy is solved using DRL, where the agent explores the optimal control strategy based on pre-set constraints in the future road environment. The geometric structure and traffic flow data of a real intersection were selected as the simulation validation environment, and a joint simulation was conducted using Python and SUMO. The experimental results indicate that in low-traffic scenarios, the queue length is reduced by more than 2 vehicles compared to the selected comparison methods; in high-traffic scenarios, the queue length is reduced by an average of 17 vehicles. Under the actual traffic data of the intersection, the average speed is increased by 6.4% compared to the fixed timing method; compared to the inductive signal control method, it increases from 9.76 m/s to 11.69 m/s, an improvement of 19.7%, effectively enhancing the intersection signal control performance.

Capacity is an important design parameter for roundabouts, and it is the premise of computing their delay and queue. Roundabout capacity has been studied for decades, and empirical regression model and gap‐acceptance model are the two main methods to predict it. Based on gap‐acceptance theory, by considering the effect of limited priority, especially the relationship between limited priority factor and critical gap, a modified model was built to predict the roundabout capacity. We then compare the results between Raff’s method and maximum likelihood estimation (MLE) method, and the MLE method was used to predict the critical gaps. Finally, the predicted capacities from different models were compared, with the observed capacity by field surveys, which verifies the performance of the proposed model.

As an important component of bike-sharing (BS) users, the travel behavior of college students and office workers is important to the promotion of BS within Chinese cities. To explore the influencing factors for the behavioral intentions of BS, this paper makes a different analysis between the two groups. Based on the theory of planned behavior, and using environmental awareness as an extended variable, a BS travel intention model was developed. A total of 676 valid questionnaires were collected and analyzed from college students and office workers in Zhengzhou. The results indicate that attitude, subjective norms, perceived behavior control, and environmental awareness have a positive impact on the behavioral intentions of BS. However, the influence degree of each variable is different between the two groups. Perceived behavior control, including travel time, travel cost, and cycling difficulty, has the greatest impact on BS behavioral intention for college students. Meanwhile, subjective norms, including policy and media publicity, has the most significant impact on BS behavioral intention for office workers. The impact of environmental awareness on college students’ use of BS is higher than that of office workers. We also found that undergraduates use BS more frequently than postgraduates. The findings provide the clear influence factors on behavioral intentions of BS between college students and office workers, that can help policy optimization in terms of bike-sharing systems, giving some suggestion for an approach devoted to deepen the individual-context interactions

As a component of the urban road network, roundabouts play a crucial role in ensuring operational efficiency. The safety performance of roundabouts significantly impacts overall traffic safety, making it necessary to conduct safety analysis and evaluation. This study utilises UAV to capture video of vehicle trajectory at roundabouts, employing the time to collision (TTC) index and vehicle evasive actions to identify and analyse traffic conflicts. A real-time traffic safety evaluation method has been developed using machine learning algorithms, including random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost) and decision tree (DT) model. This method aims to analyse the relationship between traffic states and conflicts, providing insights into potential safety risks in various traffic conditions. The four machine learning algorithms trained a total of 12 models, with RF demonstrating superior training effectiveness. It achieved high accuracy in predicting traffic conflict areas at the entrances and exits of a roundabout, with a prediction accuracy of 0.86 and an AUC (area under the receiver operating characteristic curve) of 0.88. In addition, this paper further explores the relationship between traffic conflict and state. The results show that traffic flow, speed, density, speed standard deviation and vehicle type ratio have a significant relationship to traffic conflict. This research provides valuable insights for transportation authorities to understand the nature of traffic conflicts at roundabouts, enabling them to implement appropriate early warning systems and management strategies.

Traffic accidents in urban road networks frequently result in significant congestion, further aggravating unsafe conditions. Exploring the congestion propagation from traffic accidents and proposing recovery strategies are crucial for improving urban road network efficiency. We employ the SUMO software to analyze the characteristics of network accidents, including traffic speed, density, occupancy rate, and acceleration. Then, we develop a road network recovery strategy following a traffic accident. The model consists of two parts: (1) A congestion propagation model is constructed, dividing the road network into an “accident core area” and an “accident surrounding area” (2). In the accident core area, traffic signal optimization is applied to improve traffic capacity. Meanwhile, vehicles in the accident surrounding area are guided to avoid the accident core area, thereby reducing traffic pressure. Last, a road network in Zhengzhou city was selected to verify the proposed strategy, with traffic accident points set in the network’s center and edge areas. The results show that the proposed recovery strategy effectively reduces vehicle delay after an accident. When a traffic accident occurs in the edge area of the network, the average vehicle delay is reduced by 6.73%, 16.15%, and 16.84% under low, medium, and high traffic demand, respectively. When a traffic accident occurs in the central area of the network, the average vehicle delay is reduced by 9.91%, 15.56%, and 18.80% under low, medium, and high traffic demand, respectively. Additionally, a sensitivity analysis of the strategy with respect to the traffic accident duration was conducted, demonstrating its good performance.