Coordinated ramp metering (CRM) is one effective measure to alleviate urban expressway congestion. Traditional model-based methods generally concentrate on single-bottleneck scenarios, while ignoring the case of multiple bottlenecks. In addition, the fixed-sensor fails to fully capture the dynamic traffic characteristics. The rapid development of traffic detection technology has made available a large amount of automatic vehicle identification (AVI) data, which can record detailed individual trajectories. Taking advantage of the AVI data, CRM can be improved. Besides, multi-agent deep reinforcement learning (MADRL) and game theory have been proven to be effective for traffic signal control. These methods can address the challenges faced by CRM, such as solving nonlinear and high-dimensional optimization problems. This paper proposes a distributed CRM strategy with multi-bottleneck to minimize the total travel time and balance the multiple on-ramps equity, using the individual trajectory information from AVI data. Firstly, the paper defines road segment units, road segment groups, and bottlenecks. Next, the problem is formulated as a potential game that captures the interaction among multiple bottlenecks. The controllers utilize the MADDPG algorithm to determine the green duration of the on-ramps. Finally, the proposed strategy is tested on a real-world urban expressway in a microsimulation platform SUMO. Experimental results demonstrate that the proposed strategy performs better than the baseline methods in eliminating mainline congestion and improving the multiple on-ramps equity. Compared to the no-control scenario, the proposed strategy has improved the performance of the system throughput, average travel time, and average mainline speed by 1.31%, 44.36%, and 115.23%.

It is important to achieve the real-time and reliable vehicular ad hoc networks (VANETs) for the safety and efficiency of the autonomous intersection management (AIM) system. However, there are challenges in establishing the low-delay and robustness VANETs topology in a horizon due to the high mobility, intersection shelters and malicious attacks. This paper investigates the long-horizon VANETs networking problem with the objective of producing the robust topology in the process of routing paths planning. To achieve this goal, a centralized periodic intervention networking (CPIN) approach is proposed, which consists of three main modules: long-horizon information mapping, full-path routing modeling and proactive optimization mechanism. The first module aims to provide a spatio-temporal delay tensor, considering the mobility and the quality of service (QoS). The second module converts routing path planning into a combinatorial optimization problem to achieve long-horizon full-path routing. The third module introduces a proactive optimization mechanism based on node degree centrality to evolve the VANETs networking process toward high robustness. To find the optimal solution efficiently, we propose an improved Monte Carlo Tree Search (MCTS) algorithm based on the operational characteristics of the AIM system. A series of simulation experiments are carried out to verify the effectiveness of CPIN. The results show that the CPIN outperforms the benchmark method in terms of time delay, wireless hops, and network robustness. Specifically, the CPIN reduces the average time delay by at most 44.4%-48.3% while ensuring relative high robustness.

Cooperative longitudinal–lateral trajectory optimization is essential for unmanned ground vehicle (UGV) platoons to improve safety, capacity, and efficiency. However, existing approaches often face unstable formation under low penetration rates and rely on fragmented control strategies. This study develops a cooperative longitudinal–lateral trajectory tracking framework tailored for UGV platooning, embedded in a hierarchical control architecture. Dual-stage multi-objective Model Predictive Control (MPC) is proposed, decomposing trajectory planning into pursuit and platooning phases. Each stage employs adaptive weighting to balance platoon efficiency and traffic performance across varying operating conditions. Furthermore, a traffic-aware organizational module is designed to enable the dynamic opening of UGV-dedicated lanes, ensuring that platoon formation remains compatible with overall traffic flow. Simulation results demonstrate that the adaptive weighting strategy reduces the platoon formation time by 41.6% with only a 1.29% reduction in the average traffic speed. In addition, the dynamic lane management mechanism yields longer and more stable UGV platoons under different penetration levels, particularly in high-flow environments. The proposed cooperative framework provides a scalable solution for advancing UGV platoon control and demonstrates the potential of unmanned systems in future intelligent transportation applications.

Coordinated control of ramp metering, variable speed limits, and intersection signals is critical for mitigating congestion and enhancing efficiency at urban expressway–arterial interfaces. Existing strategies often operate in isolation, leading to fragmented responses and limited adaptability under heterogeneous traffic demands. This study develops a multi-agent reinforcement learning framework based on MADDPG to achieve cooperative decision-making across heterogeneous controllers. An asynchronous control cycle mechanism is designed to accommodate different temporal requirements of ramp meters, speed limits, and signal controllers, ensuring practical feasibility in real-time operations. A conflict-aware reward design further embeds density regulation, speed harmonization, and spillback prevention to stabilize flow dynamics. Simulation experiments on a calibrated urban network demonstrate that the proposed framework delays congestion onset, reduces shockwave propagation, and improves throughput compared with classical benchmarks. In particular, at the mainline merge, average travel time is reduced to 13.56 s (62.4% of VSL-only); at the ramp, occupancy is lowered to 6.4% (40.6% of ALINEA); and at the signalized approach, average delay decreases to 85.71 s (62.7% of actuated control). These results highlight the scalability and deployment potential of the proposed cooperative control approach for system-level traffic management in mixed traffic environments.