An Optimal Vehicle-Scheduling Model for Signal-Free Intersections Considering Bus Priority in a Connected and Automated Vehicle Environment
The optimal scheduling of vehicles at signal-free intersections under the connected and automated vehicle (CAV) environment has become a research hotspot in the intelligent transportation field. However, existing studies often oversimplify the intersection’s conflict area and fail to adequately address the spatiotemporal sparsity of conflict points, with little attention given to bus priority requirements.
To address these gaps, this paper first establishes an intersection coordinate system and constructs a conflict area analysis model based on the coordinates of key conflict points and vehicle trajectories. Subsequently, an optimal scheduling model for automated vehicles at signal-free intersections with bus priority is developed, which considers the set of vehicles influencing decisions within a time window and uses vehicle entry times and lateral lane changes as decision variables.
To enhance computational speed while preserving convergence accuracy, a search space reduction method based on available gaps for conflict point traversal constraints is designed. The model is then solved using an improved double-layer multi-population particle swarm optimization (PSO) algorithm. Simulation results, compared against traditional signal control, rule-driven signal-free, and dynamic-optimization-based signal-free algorithms demonstrate that the proposed method achieves a favorable balance between computational cost and efficiency.
It significantly reduces the average vehicle delay. Moreover, incorporating bus priority reduces the average per capita delay by 18.95% compared to the non-priority scenario, effectively proving the validity of the proposed method.
This paper addresses the issue of vehicle control at signal-free intersections in the context of autonomous driving. First, grounded in an intersection coordinate system, a conflict area analysis model was constructed based on the coordinates of key conflict points and vehicle trajectories. Subsequently, considering the set of vehicles within an optimization time window that influences decision-making, an optimal scheduling model for automated vehicles at signal-free intersections with bus priority was designed, using vehicle guidance speeds on road sections and lane-changing vehicles as decision variables. At the same time, to avoid the practical limitations caused by excessively low guidance speeds and the problem of combinatorial explosion from an increased number of decision variables, the guidance of road section speeds and vehicle intersection entry times were unified. To obtain optimized solutions more efficiently, we designed an enhanced double-layer multi-population particle swarm optimization algorithm. Additionally, a search space reduction method based on available gaps for conflict point traversal constraints was proposed to improve the algorithm’s search speed in sparse spaces.
Simulation comparison results against control algorithms such as “traditional signal control,” “rule-driven signal-free control,” and “dynamic-optimization-based signal-free control” show that the method proposed in this paper can reduce vehicle delay by a maximum of 56.3–81.3%. Furthermore, a comparison between the control model considering relative bus priority and one without shows that the former reduces the per capita delay metric by 18.95% relative to the latter. These results effectively demonstrate the validity of the proposed method and can provide support for enhancing the efficiency of vehicle optimal scheduling at signal-free intersections in the context of developing autonomous driving technology.
Due to space limitations, this paper did not elaborate on the impact of passengers’ maximum tolerance time or the size of the time window on the optimization results; these issues can be addressed in subsequent research. Additionally, this paper assumes a fully automated driving environment, which to some extent limits the applicability of the proposed method. Future work can be extended to research on optimal vehicle-scheduling at signal-free intersections in mixed-traffic environments with both human-driven and automated vehicles. Finally, to strengthen the connection between the control model and practical applications, further research can be conducted on optimal control models for automated vehicles at signal-free intersections under the influence of pedestrians and non-motorized vehicles, as well as in non-fully automated environments.
Signal processing, signal strength, wireless signal, digital signal, analog signal, weak signal, strong signal, modulation, demodulation, frequency signal, noise signal, signal analysis, signal integrity, communication signal, electrical signal, biomedical signal, control signal, signal transmission, signal detection, signal measurement.
#SignalProcessing, #SignalStrength, #DigitalSignal, #AnalogSignal, #WeakSignal, #StrongSignal, #ModulationSignal, #DemodulationSignal, #FrequencySignal, #NoiseSignal, #SignalAnalysis, #SignalIntegrity, #CommunicationSignal, #ElectricalSignal, #BiomedicalSignal, #ControlSignal, #SignalTransmission, #SignalDetection, #SignalMeasurement, #WirelessSignal
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