Percolation–Stochastic Model for Traffic Management in Transport Networks

This article describes a model for optimizing traffic flow control and generating traffic signal phases based on the stochastic dynamics of traffic and the percolation properties of transport networks. As input data (in SUMO), we use lane-level vehicle flow rates, treating them as random processes with unknown distributions. It is shown that the percolation threshold of the transport network can serve as a reliability criterion in a stochastic model of lane blockage and can be used to determine the control interval. To calculate the durations of permissive control signals and their sequence for different directions, vehicle queues are considered and the time required for them to reach the network’s percolation threshold is estimated. Subsequently, the lane with the largest queue (i.e., the shortest time to reach blockage) is selected, and a phase is formed for its signal control, as well as for other lanes that can be opened simultaneously. Simulation results show that when dynamic traffic signal control is used and a percolation-dynamic model for balancing road traffic is applied, lane occupancy indicators such as “congestion” decrease by 19–51% compared to a model with statically specified traffic signal phase cycles. The characteristics of flow dynamics obtained in the simulation make it possible to construct an overall control quality function and to assess, from the standpoint of traffic network management organization, an acceptable density of traffic signals and unsignalized intersections.