To contribute to the development of sustainable transport that is safe, eco-friendly, and efficient, this research proposed a safe and ecological speed control system for heavy-duty vehicles on long–steep downhill and sharp-curved roads under a partially connected vehicles environment consisting of connected heavy-duty vehicles (CHDVs) and conventional human-driven vehicles. This system prioritizes braking and lateral motion safety before improving fuel efficiency and ensuring traffic mobility at optimal status, and optimizes the speed trajectories of CHDVs to control the entire traffic. Speed optimization is modelled as an optimal control problem and solved by the iterative Pontryagin’s maximum principle algorithm. The simulation-based evaluation shows that the proposed system effectively reduces the peak temperature of the brake drums, the lateral slip angle of the vehicle wheels, and the lateral load transfer rate of the vehicle body; all these measurements of effectiveness are limited to safe ranges. A detailed investigation reveals that the proposed system reduces fuel consumption by up to 15.49% and inhibits the adverse effects on throughput. All benefits increase with the market penetration rate (MPR) of CHDVs and the traffic congestion level and reach significant levels under low MPRs of CHDVs. This indicates that the proposed system has good robustness for the impedance from conventional vehicles and could be implemented in the near future.

This study evaluated the performance of an eco-approach control system at signalized intersections under a partially connected and automated vehicle (CAV) environment. This system has the first eco-approach controller able to function with the existence of surrounding human-driven traffic. A previous evaluation only confirmed its benefits. The purpose of this study was to conduct a further extensive test on the controller to identify room for improvement. Two different networks were tested, including an isolated signalized intersection and a corridor with two signalized intersections. The measures of effectiveness (MOEs) adopted were throughput and fuel consumption. All the before-and-after MOEs were compared using t-tests. The results indicate that the controller generally improved the fuel efficiency without harm to the mobility, and its environmental performance was affected by the minimum CAV speed, green ratio, congestion level, and marker penetration rate of CAVs. A detailed investigation revealed that no significant environmental benefit was generated under high congestion levels when the minimum speed of CAVs was more than 20 mph, and the shockwaves caused by the eco-approach control may result in a gating effect that reduces the throughput at the upstream intersection of the corridor under high congestion levels.

Accurate bus passenger flow prediction is a critical prerequisite for optimizing resource allocation in intelligent bus systems and improving public transport service quality. However, due to dynamic fluctuations in passenger travel demand and the complex topological structure of bus networks, effectively capturing the inherent spatiotemporal dependencies of passenger flow remains a significant challenge. To address this issue, this paper proposes GAT-Informer, a hybrid deep learning model for short-term bus passenger flow prediction. Unlike conventional methods that mainly rely on fixed physical adjacency or single-view spatial correlations, the proposed model incorporates domain knowledge into graph construction through a four-dimensional associated-node mechanism. Specifically, four types of features, namely line connectivity, spatial proximity, OD correlation, and station functional similarity, are used to identify relevant associated nodes and construct a passenger flow interaction spatiotemporal graph that better reflects inter-station dependencies. Based on this graph, a Graph Attention Network (GAT) is introduced to adaptively learn spatial interaction features and differentiated influence weights among associated stations. The spatially enhanced features extracted by GAT are then fed into the Informer network, where probabilistic sparse self-attention and hierarchical timestamp encoding are employed to efficiently capture long-term temporal dependencies and periodic fluctuation patterns of passenger flow. Experimental results based on bus passenger flow data from Foshan City show that the proposed GAT-Informer model significantly outperforms benchmark models, including LSTM-Transformer, CNN-GRU, and STGCN, across different prediction horizons, validating its effectiveness and improved predictive performance in bus passenger flow prediction.

To solve the problem of insufficient turning radius for heavy-duty wreckers in narrow and congested road environment, this paper designed a new type of in-situ slewing device, and conducted a finite element simulation analysis on it. On this basis, by combining the optimization theory, this paper further optimized the structure of in-situ slewing device by using three different optimization design methods, including screening method, nonlinear second-order Lagrange method and quadratic integer sequence algorithm. The total weight loss of outrigger structure was 69.96 kilogram, the weight-loss ratio is equal to 7.34%. These results prove that the goal of structural optimization design has been achieved.