Aug 2026· Systems· Vol 14, pp. 919· 0 citations· 28 references
Abstract
Relying solely on NEMA phases for urban green-wave control often restricts the feasible regions of network optimization, resulting in narrow bandwidths or unsolvable models. To address this limitation, this paper proposes a mixed-integer linear programming model for regional signal coordination based on a mixed-phase release strategy that integrates NEMA dual-ring phase and split phase. By utilizing shared lanes under split phasing, the model maximizes lane resource efficiency and extends coordination benefits to left-turn traffic. Introducing 0–1 decision variables establishes a unified formulation for internal phase offsets, enabling flexible, intersection-specific release selection. To balance network efficiency and fairness, the optimization objective minimizes the weighted sum of the red-wave bandwidth-to-cycle ratio, subject to spatiotemporal and clockwise closed-loop constraints. A real-world case study in Suzhou, solved via the branch-and-bound method, demonstrates that the optimal design deploys split phase at seven intersections and NEMA phases at two. VISSIM simulations confirm that compared to the NEMA-only approach, the proposed mixed model reduces red-wave bandwidth by 49.52%, average delays by 26.36%, and stops by 17.5%. The proposed model provides a system-level signal coordination framework for improving the adaptability and reliability of urban traffic control systems.
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.
Friday Idakwo David, S. T. Apeh, Oduware Okosun· E3S Web of Conferences· 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.
To address the limitations of existing models for mixed networks comprising expressways and arterial regions, this study develops a unified modeling and control framework. First, within such a mixed network, the trip length characteristics of urban trips are analyzed, revealing marked differences in trip lengths between trips leaving the network via arterial regions and those leaving via expressways through on-ramps. This heterogeneity is incorporated into the modeling process to reflect realistic travel patterns. Second, a hybrid traffic model is proposed by integrating a multi-class cell transmission model (CTM) for expressways with a combination of trip-based and accumulation-based macroscopic fundamental diagrams (MFDs) for urban regions. The trip-based MFD captures flow heterogeneity through remaining distance distributions, while the accumulation-based formulation enables tractable control. This integration ensures consistent route-based state representation across subsystems. Third, a route choice model is established, and a coordinated control strategy is developed under a model predictive control (MPC) framework, jointly optimizing route guidance, ramp metering, and perimeter control. The case study demonstrates that the proposed cooperative strategy effectively alleviates congestion and enhances network efficiency compared with flow control alone, with its performance further influenced by the level of compliance. Note to Practitioners—Mixed networks composed of urban arterials and expressways are widespread in cities. Because the two subsystems are coupled through ramps, the mechanisms of congestion become more complex and harder to manage; existing practice often manages the two networks separately, making it difficult to balance pressure at the overall network level in a timely manner. This paper proposes a coordinated method of route guidance and flow control for mixed networks. First, an integrated traffic model is established to characterize the dynamics of each urban region and expressway segment and the flow exchanges between subsystems, providing operators with a unified basis for understanding system states and interactions. On this basis, a coordinated route guidance and flow control scheme is introduced to mitigate congestion: route guidance allocates travel demand at origins to optimize the spatial distribution of flows across the network, while boundary flow control-implemented via ramp metering and perimeter control-dynamically regulates flow exchange between expressways and urban regions. For implementation, ramp metering and perimeter control can be deployed using existing signal controllers or ramp signals, and route guidance can be disseminated through navigation platforms or traveler information systems. A case study on a real network shows that, compared with flow control alone, the coordinated strategy further reduces network congestion and alleviates boundary queuing. The effectiveness of the method depends on network-specific calibration of trip-length distributions and on driver compliance with guidance. Future work may extend the study to intercity freeway corridors to further examine applicability under different spatial scales and demand structures.
Yunran Di, Weihua Zhang, Heng Ding et al.· IEEE Transactions on Automat...· 0 citations
Traffic congestion results in increased travel times and frequent delays. This paper introduces a novel speed harmonization perimeter controller (SHPC) that integrates variable speed limit control with sliding mode theory and is evaluated using the INTEGRATION microscopic traffic simulator. The proposed controller adopts a speed-based perimeter control strategy by regulating the speed at the gated links of the protected network, rather than modifying traffic signal timings. The developed controller was first applied to a medium-sized grid network inspired by downtown Washington, DC, and compared with two benchmark controllers: a fixed-time plan (FP) controller and a decentralized adaptive traffic signal controller that optimizes phase splits and cycle lengths (PSC). At the entire-network level, SHPC reduced queue length and total delay by 24.7% and 31.2%, respectively, relative to FP, and by 11.7% and 19.8%, respectively, relative to PSC. SHPC also reduced travel time, fuel consumption, and CO2 emissions. At the protected-network level, SHPC further reduced queue length and total delay by 26.1% and 31.0%, respectively, relative to FP, and by 9.9% and 9.8%, respectively, relative to PSC, while also improving the remaining measures of effectiveness (MOEs). The controller was then evaluated on a large-scale downtown Los Angeles network. Due to the size and heterogeneity of this network, a geographical self-organizing map (GeoSOM) was developed as a preprocessing step to identify a homogeneous congested region suitable for perimeter control. At the entire-network level, SHPC reduced queue length and total delay by 15.6% and 14.7%, respectively, relative to FP, and by 5.4% and 4.7%, respectively, relative to PSC. Similar reductions were achieved in the remaining MOEs. At the protected-network level, SHPC reduced queue length and total delay by 9.4% and 4.1%, respectively, relative to FP, and by 4.5% and 5.1%, respectively, relative to PSC, with corresponding improvements in the remaining MOEs. These results demonstrate that the proposed SHPC framework can be effectively scaled to large-scale urban networks while improving mobility, mitigating traffic congestion, and providing environmental benefits.
M. Elouni, H. Rakha, Mónica Menéndez et al.· IEEE Access· 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
Predefined low-altitude corridors create a coupled routing–scheduling problem when multiple drone routes enter the same controlled segment. This study separates an upstream control hub from its scarce directed hub–segment resource and develops an event-expanded continuous-time mixed-integer linear programming model with optional fleet activation, complete-route energy and capacity checks, release precedence, minimum entry headway, holding, and downstream delay propagation. A headway-aware large neighborhood search (HA-LNS) combines route neighborhoods with a finite serial event decoder. Gurobi proves optimality on three small instances, and fixed-route timing MILPs exactly match the decoder, including for a repeated physical-hub visit. Across ten matched networks per scale, HA-LNS changes the mean objective relative to route-only LNS by 0.01%, 0.90%, and 2.33% at nominal scales 30, 50, and 100. Under high conflict-resource density, the reduction reaches 5.77%, while mean holding falls from 2.054 to 0.025 min. Simulated annealing is 1.04% better at scale 50 and statistically indistinguishable at scales 30 and 100, showing that the contribution is conflict-aware integration rather than universal heuristic dominance. The framework identifies directed-resource density as the main condition under which temporal coordination materially improves route decisions.