Specifically, we integrate two essential sub-tasks, i.e., traffic data imputation and decision-making, by leveraging a Partial Rewards Conditioned Diffusion (PRCD) model to prevent missing rewards

Vehicle collisions remain a major challenge in large-scale mixed traffic systems, especially when human-driven vehicles (HVs) and robotic vehicles (RVs) interact under dynamic and uncertain conditions. Although Multi-Agent Reinforcement Learning (MARL) offers promising capabilities for traffic signal control, ensuring safety in such environments remains difficult. As a direct indicator of traffic risk, the collision rate must be well understood and incorporated into traffic control design. This study investigates the primary factors influencing collision rates in a MARL-governed Mixed Traffic Control (MTC) network. We examine three dimensions: total vehicle count, signalized versus unsignalized intersection configurations, and turning-movement strategies. Through controlled simulation experiments, we evaluate how each factor affects collision likelihood. The results show that collision rates are sensitive to traffic density, the level of signal coordination, and turning-control design. These findings provide practical insights for improving the safety and robustness of MARL-based mixed traffic control systems, supporting the development of intelligent transportation systems in which both efficiency and safety are jointly optimized.

Urban traffic congestion stems from the misalignment between self-interested routing decisions and socially optimal flows. Intersections, as critical bottlenecks, amplify these inefficiencies because existing control schemes often neglect drivers' strategic behavior. Autonomous intersections, enabled by vehicle-to-infrastructure communication, permit vehicle-level scheduling based on individual requests. Leveraging this fine-grained control, we propose a non-monetary mechanism that strategically adjusts request timestamps-delaying or advancing passage times-to incentivize socially efficient routing. We present a hierarchical architecture separating local scheduling by roadside units from network-wide timestamp adjustments by a central planner. We establish an experimentally validated analytical model, prove the existence and essential uniqueness of equilibrium flows and formulate the planner's problem as an offline bilevel optimization program solvable with standard tools. Experiments on the Sioux Falls network show up to a 68% reduction in the efficiency gap between equilibrium and optimal flows, demonstrating scalability and effectiveness.

Connected Autonomous Vehicles (CAVs) promise to reduce congestion in future urban networks, potentially by optimizing their routing decisions. Unlike for human drivers, these decisions can be made with collective, data-driven policies, developed using machine learning algorithms. Reinforcement learning (RL) can facilitate the development of such collective routing strategies, yet standardized and realistic benchmarks are missing. To that end, we present URB: Urban Routing Benchmark for RL-equipped Connected Autonomous Vehicles. URB is a comprehensive benchmarking environment that unifies evaluation across 29 real-world traffic networks paired with realistic demand patterns. URB comes with a catalog of predefined tasks, multi-agent RL (MARL) algorithm implementations, three baseline methods, domain-specific performance metrics, and a modular configuration scheme. Our results show that, despite the lengthy and costly training, state-of-the-art MARL algorithms rarely outperformed humans. The experimental results reported in this paper initiate the first leaderboard for MARL in large-scale urban routing optimization. They reveal that current approaches struggle to scale, emphasizing the urgent need for advancements in this domain.

Intelligent transport systems planning is a research area that offers a variety of problems that are interesting from a mathematical point of view. In the last decades, due to technology evolution, one could argue that such planning might be enhanced and optimized by the use of satellite navigation devices that offer real-time information. However, as expressed in [9] and analyzed in [6], the use of such technologies generates the displacement of congestion from one zone to another. It is believed that such effects are a consequence of not knowing the travel choice patterns of users and making no behavior prediction. Hence, it is still very useful and valuable to study a deterministic and static version of the problem.

Traffic Signal Control (TSC) is essential for managing urban traffic flow and reducing congestion. Reinforcement Learning (RL) offers an adaptive method for TSC by responding to dynamic traffic patterns, with multi-agent RL (MARL) gaining traction as intersections naturally function as coordinated agents. However, due to shifts in environmental dynamics, implementing MARL-based TSC policies in the real world often leads to a significant performance drop, known as the sim-to-real gap. Grounded Action Transformation (GAT) has successfully mitigated this gap in single-agent RL for TSC, but real-world traffic networks, which involve numerous interacting intersections, are better suited to a MARL framework. In this work, we introduce JL-GAT, an application of GAT to MARL-based TSC that balances scalability with enhanced grounding capability by incorporating information from neighboring agents. JL-GAT adopts a decentralized approach to GAT, allowing for the scalability often required in real-world traffic networks while still capturing key interactions between agents. Comprehensive experiments on various road networks under simulated adverse weather conditions, along with ablation studies, demonstrate the effectiveness of JL-GAT. The code is publicly available at https://github.com/DaRL-LibSignal/JL-GAT/.

--Traffic congestion remains a major challenge for modern urban transportation, diminishing both efficiency and quality of life. While autonomous driving technologies and reinforcement learning (RL) have shown promise for improving traffic control, most prior work has focused on small-scale networks or isolated intersections. Large-scale mixed traffic control, involving both human-driven and robotic vehicles, remains underexplored. In this study, we propose a decentralized multi-agent reinforcement learning framework for managing large-scale mixed traffic networks, where intersections are controlled either by traditional traffic signals or by robotic vehicles. We evaluate our approach on a real-world network of 14 intersections in Colorado Springs, Colorado, USA, using average vehicle waiting time as the primary measure of traffic efficiency. We are exploring a problem that has not been sufficiently addressed: Is large-scale Multi-Agent Traffic Control (MTC) still feasible when facing time-varying Origin-Destination (OD) patterns?

Accurately estimating the impact of road maintenance schedules on traffic conditions is important because maintenance operations can substantially worsen congestion if not carefully planned. Reliable estimates allow planners to avoid excessive delays during periods of roadwork. Since the exact increase in congestion is difficult to predict analytically, traffic simulations are commonly used to assess the redistribution of the flow of traffic. However, when applied to long-term maintenance planning involving many overlapping projects and scheduling alternatives, these simulations must be run thousands of times, resulting in a significant computational burden. This paper investigates the use of machine learning-based surrogate models to predict network-wide congestion caused by simultaneous road renovations. We frame the problem as a supervised learning task, using one-hot encodings, engineered traffic features, and heuristic approximations. A range of linear, ensemble-based, probabilistic, and neural regression models is evaluated under an online learning framework in which data progressively becomes available. The experimental results show that the Costliest Subset Heuristic provides a reasonable approximation when limited training data is available, and that most regression models fail to outperform it, with the exception of XGBoost, which achieves substantially better accuracy. In overall performance, XGBoost significantly outperforms alternatives in a range of metrics, most strikingly Mean Absolute Percentage Error (MAPE) and Pinball loss, where it achieves a MAPE of 11% and outperforms the next-best model by 20% and 38% respectively. This modeling approach has the potential to reduce the computational burden of large-scale traffic assignment problems in maintenance planning.

Lei et al.- Traffic Adaptive Moving-window Patrolling Algorithm 1 Traffic Adaptive Moving-window Service Patrolling for Real-time Incident Management during High-impact Events Haozhe Lei a, Y a-Ting Y ang a, Tao Li a, Zilin Bian b,, Fan Zuo b, Sundeep Rangan a, and Kaan Ozbay b a Department of Electrical and Computer Engineering, New Y ork University, United States of America b Department of Civil and Urban Engineering, New Y ork University, United States of AmericaKeywords: High-impact event management, service patrol, dynamic programming, adaptive graph This paper presents the Traffic Adaptive Moving-window Patrolling Algorithm (T AMP A), designed to improve real-time incident management during major events like sports tournaments and concerts. Such events significantly stress transportation networks, requiring efficient and adaptive patrol solutions. Using dynamic programming, the algorithm continuously adjusts patrol strategies within short planning windows, effectively balancing immediate response and efficient routing. Theoretical analyses ensure performance remains closely aligned with optimal solutions. Simulation results from an urban traffic network demonstrate T AMP A's superior performance, showing improvements of approximately 87.5% over stationary methods and 114.2% over random strategies. Future work includes enhancing adaptability and incorporating digital twin technology for improved predictive accuracy, particularly relevant for events like the 2026 FIFA World Cup at MetLife Stadium.1 Introduction 1.1 Motivation Organizing high-impact events, such as sports tournaments, festivals, and concerts, presents substantial social, economic, and transportation challenges. These events can place immense pressure on transportation infrastructure, security protocols, and public services, particularly in regions that are already congested and economically vital, such as the New Y ork-New Jersey (NYNJ) or Los Angeles (LA) metropolitan* Corresponding author. Both regions attract large, diverse crowds as tourists from across state lines and around the world, further complicating special event management logistics. A prime example of this challenge is the hosting of mega-events, such as the FIFA World Cup Ardemagni (2022) and the Olympics (Government, 2022; Harrison, 2021).