Max-pressure (MP) control has emerged as a prominent real-time network traffic signal control strategy due to its simplicity, decentralized structure, and theoretical guarantees of network queue stability. Meanwhile, advances in connected vehicle (CV) technology have sparked extensive research into CV-based traffic signal control. Despite these developments, few studies have investigated MP control in heterogeneously distributed and partially CV environments while ensuring network queue stability. To address these research gaps, we propose a CV-based MP control (CV-MP) method that leverages real-time CV travel time information to compute the pressure, thereby incorporating both the spatial distribution and temporal delays of vehicles, unlike existing approaches that utilized only spatial distribution or temporal delays. In particular, we establish sufficient conditions for road network queue stability that are compatible with most existing MP control methods. Moreover, we pioneered the proof of network queue stability even if the vehicles are only partially connected and heterogeneously distributed, and gave a necessary condition of CV observation for maintaining the stability. Evaluation results on an Amsterdam corridor show that CV-MP significantly reduces vehicle delays compared to both actuated control and conventional MP control across various CV penetration rates. Moreover, in scenarios with dynamic traffic demand, CV-MP achieves lower spillover peaks even with low and heterogeneous CV penetration rates, further highlighting its effectiveness and robustness.

With the emerging Internet of Things (IoT) and Vehicle-Road-Cloud Integration System (VRCIS) technologies, coordinating Connected and Automated Vehicles (CAVs) and traffic signal is becoming a practical solution to further enhance traffic efficiency. However, current studies still have limitations. Firstly, there is a domain mismatch between CAV trajectory planning (temporal domain) and signal optimization (spatial domain). This mismatch requires separate modeling of trajectory planning and signal optimization, which greatly reduces global optimality. Secondly, previous studies are not applicable to actual mixed traffic environment, since they mostly simplify Human-driven Vehicle’s (HV) behavior without considering queuing and stop-and-go maneuvers. Therefore, we propose a novel Multi-Vehicles and Signal Cooperation (MVSC) planner to solve the limitations via following designs. (i) Joint optimization is achieved via formulating in the spatial domain, unifying CAV’s planning domain with traffic signal optimizing domain. (ii) A parallel algorithm is designed for the adaptation to numbers of CAVs. This algorithm is based on Alternating Direction Method of Multipliers (ADMM), making full use of IoT and VRCIS. (iii) HV queuing and stop-and-go behaviors are considered in our modeling. Simulation results show that the proposed MVSC planner can enhance efficiency and ecology by 23.60% and 15.63%. At CAV’s penetration rate of 40% and V/C ratio of 0.75, the proposed planner shows its full potential in performance enhancement. The average computation time of parallel computing approach is only within 10 milliseconds, which confirms the real-time implementation capability.

The increasingly complicated urban traffic patterns lead traffic signal control to a new trend of higher flexibility and quicker response, which becomes possible with advances in both sensor technology and artificial intelligence. Though in its early stage, existing intelligent signal controllers equipped with reinforcement learning (RL)-based feature extractor and large language model (LLM)-driven scenario understanding and decision support already demonstrate powerful data digesting ability. This study thus proposes a smart traffic light control system integrating a vision-based perception tool to extract traffic state from real-time snapshot image of the intersection, and an LLM agent controller for signal phase switching upon scenario analysis. An indicator describing the urgency for green time at phase level is defined to abstract the contextual information regarding the competition of multiple approaching traffic flows, which augments the LLM with domain-specific logical reasoning for signal control action generation, aimed at assigning green time to the flows with the most compelling needs. With a RL-based controller providing initial control decision as backup, the proposed method is able to handle both pre-trained and out-of-distribution scenarios through real-time traffic state diagnosis and knowledgeable reasoning. Simulation evaluation on different intersection layouts and vehicle compositions is conducted with horizontal comparison of five benchmarks. A decrease in average waiting time was realized by more than 5 % under normal traffic scenario and 20 % under emergency vehicle scenario, respectively. Further, comprehensive analysis was conducted to explore the applicability of the proposed method and feasibility for real-world application in unmanned aerial vehicle (UAV)-based intelligent traffic management.

Max-pressure (MP) control has proven effective at stabilizing network queues and improving traffic throughput in large-scale urban road networks. However, conventional MP controllers based on connected vehicle (CV) data face two critical limitations: network stability diminishes when connected vehicle (CV) penetration rates are low, and significant privacy concerns arise when utilizing individual vehicle data. To address these challenges, this paper proposes a novel Private-MP controller that fuses data from both fixed-location detectors and CVs in an architecture of mobile edge computing. To fully safeguard CV privacy, including macro-route information and micro-trajectory information, Private-MP employs a privacy-preserving mechanism that combines homomorphic encryption with an adaptive randomized response strategy. Simulation studies on a network with five intersections showed that despite some increases in average vehicle delay due to privacy protection, Private-MP still ensures a more robust performance on average vehicle delay than CV-based MP in low penetration rate scenarios and outperforms traditional detector-based MP control while improving fairness among connected and non-connected vehicles.

Multi-section license plate recognition (LPR) data has emerged as a valuable source for lane-based queue length estimation, providing both input–output information and sampled travel times. However, existing studies often rely on restrictive assumptions such as the first-in-first-out (FIFO) rule and uniform arrival processes, which fail to capture the complexity of multi-lane scenarios, particularly regarding overtaking behaviors and traffic flow variations. To address this issue, we propose a probabilistic approach to derive the stochastic queue length by constructing a conditional probability model of no-delay arrival time (NAT), i.e., the arrival time of vehicles without experiencing any delay, based on multi-section LPR data. First, the NAT conditions for all vehicles are established based on upstream and downstream vehicle departure times and sequences. To reduce the computational dimensionality and complexity, a dynamic programming (DP)-based algorithm is developed for vehicle group partitioning based on potential interactions between vehicles. Then, the conditional probability of NATs of each vehicle group is derived and a Markov Chain Monte Carlo (MCMC) sampling method is employed for calculation. Subsequently, the stochastic queue profile and maximum queue length for each cycle can be derived based on the NATs of vehicles. Eventually, we extend our approach to multi-lane scenarios, where the problem can be converted to a weighted general exact coverage problem and solved by a backtracking algorithm with heuristics. Empirical and simulation experiments demonstrate that our approach outperforms the baseline method, demonstrating significant improvements in accuracy and robustness across various traffic conditions, including different V/C ratios, matching rates, miss detection rates, and FIFO violation rates. The estimated queue profiles demonstrate practical value for offset optimization in traffic signal control, achieving a 6.63% delay reduction compared to the conventional method.

Smart traffic systems, like those using wellestablished methods such as SCOOT, SCATS and TUC, aim to improve traffic flow by dynamically adjusting signal timings based on real-time traffic conditions. Traffic engineers need to understand the objective functions behind traffic signal control to analyze, improve, and optimize network performances. However, different jurisdictions, different operators and competing interests imply that the underlying objective functions governing traffic signal control might not be publicly known with sufficient detail (e.g. to preserve Intellectual Property Rights). A method for discovering these functions is therefore needed, particularly to enable better cooperation among stakeholders. In this work, we train computer models to mimic the decisions made by smart traffic light systems. Using data from a simulated traffic network (with virtual sensors tracking vehicles), we test a variety of supervised models, ranging from simple decision trees to more complex neural networks. Our results show these models can accurately mimic the underlying system's actions, achieving up to 99% accuracy. This work demonstrates that supervised learning can serve as a powerful tool for uncovering hidden traffic control functions by training models to replicate the system's decisions. By analyzing these models, we can then infer the key factors influencing signal control, thereby gaining insights into the underlying objective function.

Cycle-based arrival profiles can describe temporal demand distribution within a signal cycle for signalized intersections, which can be used to calculate indicators such as traffic volume, queue length, and facilitate fine-grained signal control. However, few studies address cycle-level arrival profile estimation based on connected vehicles (CVs). Besides, studies addressing privacy issues for cross-company collaboration in traffic management are still in their infancy. To fill these research gaps, this study proposes a data-driven method for privacy-preserving cycle-based arrival profile estimation using cross-company CV data. The cyclic arrival curve is discretized as an arrival rate vector whose elements are calculated using sampled CV trajectories, thus transforming the arrival profile estimation into a matrix completion problem. Considering cross-company collaboration, a privacy technique, secure multi-party computation, is used to encrypt initial arrival rate matrices of multiple companies. In particular, a perturbation approach is combined to enhance protection against inference attacks with prior knowledge of the matrix construction process. Then, matrix completion is realized through a singular value thresholding (SVT) algorithm, meanwhile achieving denoising. Empirical evaluation shows that the estimation accuracy of traffic volume and queue length derived from the proposed arrival profile estimation method can reach 87.6% and 78.4%, respectively, meanwhile protecting the privacy of multiple participating companies and outperforming existing methods. Simulation evaluation on a large-scale network further demonstrates the reliability of the proposed method considering ever-changing demand scenarios. A comprehensive sensitivity analysis exhibit its robustness to CV sample size, number of participating parties and data disparity, showing wide popularization and application prospects.

Complex traffic scenes greatly challenge the road safety of automated vehicles (AVs). Recent work only provides an independent perspective from the fundamental modules. This paper integrates the decision-making and path-planning modules to ensure the autonomous driving performance in the high-speed cruising scenario. First, to guarantee deep exploration of the reinforcement learning method, a Bootstrapped deep-Q-Network (BDQN) is proposed to address the adaptive decision-making of AVs. Then, quantifying the multi-performance requirements of AVs under high-speed cruising can be complex. We employ an inverse reinforcement learning (IRL) approach to learn path-planning ability from skilled drivers, generating a reference path for executing lane changes. The simulation results demonstrate the proposed framework can ensure the autonomous cruising performance with safety guarantees.