A demand-driven signal control strategy is developed to allocate green time based on real-time vehicle demand, eliminating wasted signal phases and providing a scalable and intelligent solution for modern smart city traffic systems.
Abstract
Efficient traffic management at urban intersections remains a major challenge due to increasing demand and complex network interactions. This paper presents a generalized mathematical traffic flow model for a signalized intersection that dynamically communicates with four neighbouring intersections (ahead, behind, left, and right). The model integrates a state-space representation to capture queue dynamics, arrival rates, and interconnection effects within a unified framework. A demand-driven signal control strategy is developed to allocate green time based on real-time vehicle demand, eliminating wasted signal phases. The framework further incorporates Internet of Things (IoT)-based sensing for real-time data acquisition and inter-intersection communication, alongside an Artificial Intelligence (AI)-based optimization layer for adaptive decision-making. A Petri Net supervisory control mechanism is introduced to manage signal transitions and enable emergency vehicle pre-emption. These results were obtained through MATLAB-SUMO co-simulation across multiple traffic scenarios. Results demonstrate significant improvements, including reduced queue length and delay, increased throughput, and enhanced congestion management compared to conventional methods. The proposed model provides a scalable and intelligent solution for modern smart city traffic systems.
Emergency vehicle (EV) progression at consecutive arterial intersections in an intelligent connected environment is often disrupted by upstream queues, downstream spillback, and mismatches in multi-intersection signal coordination. To address this problem, this study proposes a spatiotemporal graph multi-agent proximal policy optimization (STG-MAPPO) method for EV signal priority control. The proposed method models consecutive signalized intersections as cooperative agents, incorporates EV arrival prediction, target-lane yielding capacity, and downstream blockage risk into the state representation, and uses a spatiotemporal graph encoder to capture both topological coupling and traffic-state propagation among intersections. Heterogeneous yielding responses of connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) are explicitly considered in the target-lane yielding model. Simulation experiments based on the Zhengzhou Longhu Autonomous Driving Test Zone show that STG-MAPPO achieves better EV progression efficiency, lower general-traffic delay, and improved safety indicators than fixed-time control, actuated control, rule-based EV priority, DDQN, IPPO, and MAPPO. Under the specified SUMO-based single-EV simulation conditions, the proposed method reduces EV travel time, EV delay, and EV stop frequency by 22.28%, 51.02%, and 60.00%, respectively, compared with DDQN, and by 8.54%, 22.58%, and 33.33%, respectively, compared with MAPPO.
Sen Cao, Xingchen Zhang, Wenfang Li et al.· Scientific Reports· 0 citations
This book presents an arterial green-wave synchronous coordination model for bus and non-bus lanes based on platoon dispersion theory. As the traffic light at an upstream intersection change from red to green, the dispersive characteristics of these vehicles moving from upstream to the downstream were analyzed by assuming velocities of two platoon following a normal distribution pattern. The model aims at analyzing relationship between traffic flow, distance between adjacent intersections, and signaling time in order to achieve arterial green-wave synchronous coordination in both the bus and non-bus lanes. To facilitate coordination in a traffic signal control system, the number of vehicles forced to stop at the head of the platoon as well as the number of vehicles trapped at the tail of the platoon were deter-mined and presented in a tabular form for use in the proposed traffic light coordination model. Finally, a numeric computation for the coordination of successive signals is presented to illustrate the validity of the proposed model.
This study integrates predictive traffic inference, GIS-based coordination, and adaptive guidance generation into a scalable ITS architecture, providing a viable foundation for next-generation smart expressway management.
S. Hirekhan, A. Hirekhan, C. Waghmare et al.· African Journal Of Applied R...· 0 citations
In signalized urban road networks, periodic signal phase switching is a key factor influencing traffic-flow stability and operational efficiency. With the rapid development of Connected and Automated Vehicle (CAV) technologies, exploiting their enhanced perception, communication, and cooperative control capabilities has become an important research topic. To characterize the acceleration, deceleration, queueing, and discharge disturbances induced by signal phase transitions, this study proposes a Signal-Constrained Optimal Velocity Model (SC-OVM). By introducing a continuous signal decision function, the proposed model dynamically couples traffic signal states with vehicle-following behavior, including preceding-vehicle following and stop-line tracking within a unified optimal-velocity framework. Furthermore, linear stability analysis, boundary critical condition analysis, and disturbance probability modeling are integrated to reveal the instability mechanism caused by abrupt signal phase transitions, with extensions to stochastic prediction errors and adaptive Signal Phase and Timing (SPaT) inputs. Numerical simulations show that SC-OVM-controlled CAVs can smooth vehicle trajectories, reduce average delay, improve end-of-green passing performance, and achieve a balanced performance in efficiency, stability, and safety compared with the Full Velocity Difference Model (FVDM), Virtual Leading Vehicle model (VLV), and Intelligent Driver Model (IDM). The findings provide theoretical support and practical insights for stability modeling and cooperative control of mixed-traffic flow at signalized intersections.
Menghan Du, Jiangchen Li, Mengyuan Sun et al.· Electronics· 0 citations
Driven by the swift pace of urbanization and the ongoing expansion of automobile ownership, congestion in city traffic has emerged as a major challenge hindering urban progress, causing heightened commuting delays, raised fuel usage, and worsened atmospheric pollution. Conventional fixed-time traffic signal management systems struggle to accommodate the fluid and intricate nature of urban vehicular flows, leading to suboptimal utilization of pavement assets. To address this issue, this study introduces a multi-objective cooperative optimization framework for urban traffic signal coordination utilizing an enhanced genetic algorithm (IGA) and game theory architecture. Initially, a multi-faceted optimization model for signal timing is constructed, prioritizing minimal average vehicle delay, maximal intersection throughput, and reduced carbon discharges as primary optimization goals. Then, the genetic algorithm is refined by incorporating adaptive crossover and mutation likelihoods along with an elite preservation mechanism to prevent early convergence and enhance global exploration capacity. Subsequently, the game theory framework is merged to harmonize the diverse interests of various intersections, converting the timing adjustment task into a non-cooperative game among nodes, and achieving collective regional traffic signal coordination via Nash equilibrium resolution. Ultimately, simulation trials are conducted utilizing actual traffic datasets from a prototypical urban roadway network through VISSIM simulation tools. Findings indicate that relative to traditional genetic algorithms and fixed-time control techniques, the suggested strategy decreases average vehicle delay by 28.3%, boosts intersection throughput by 19.7%, and lowers carbon emissions by 16.2%, which significantly enhances the functional efficiency of urban transportation systems and attains multi-objective collaborative optimization of traffic flow, energy efficiency, and ecological sustainability. This investigation offers a novel technical methodology for urban traffic signal regulation and advanced intelligent transportation governance.
Xin Yu· International Conference on...· 0 citations
The coordination of Automated Guided Vehicles (AGVs) in high-density industrial environments represents a critical challenge within Logistics 4.0, as traditional traffic management methods often lead to inefficiencies caused by negotiation-based priority assignment. To overcome the resulting limitations, this paper presents an innovative AGV traffic management system based on a Lifelong Multi-Agent Path Finding (L-MAPF) algorithm operating on roadmaps generated with Non-Uniform Rational B-Splines (NURBS) curves. The approach guarantees locally optimal coordination and ensures safe operation of large and heterogeneous AGVs. Building on this concept, the proposed framework integrates a modified version of the Bounded Horizon Conflict Based Search (CBS) technique within a Rolling Horizon Conflict Resolution strategy, utilizing an extended time horizon for each agent to enable effective conflict resolution in corridors identified by a topological map. In contrast to state-of-the-art methods for AGV fleet traffic management, the proposed solution is designed for real-world, non-standardized (i.e., non-grid-like) industrial settings characterized by narrow bidirectional corridors and high-traffic density, where AGVs of various sizes and capabilities operate simultaneously. Key contributions include an anytime conflict resolution strategy with adaptive time horizon regulation, an execution layer for safe and standard-compliant interaction with real AGVs, and an advanced mechanism for deadlock detection and resolution. Experimental results obtained in realistic industrial environments demonstrate higher throughput, with improvements of up to 11% over a conventional rule-based traffic management system, a state-of-the-art industrial method, and a priority-based L-MAPF variant, while maintaining continuous operation and improved efficiency.
A. Bonetti, Silvia Proia, Simone Guidetti et al.· The international journal of...· 0 citations