A self-organizing system for urban traffic control based on predictive interval microscopic model

TL;DR

This paper presents a self-organizing urban traffic control system using interval microscopic models, enabling agents to predict and adapt to traffic conditions with uncertainty, improving performance especially under non-uniform traffic flows.

Contribution

It introduces a novel interval microscopic traffic prediction model for self-organizing traffic signals, enhancing adaptability and robustness in urban traffic management.

Findings

Improved traffic flow in simulations with non-uniform traffic streams

Agents effectively recognize and suspend uncertain control actions

Enhanced overall traffic performance compared to traditional methods

Abstract

This paper introduces a self-organizing traffic signal system for an urban road network. The key elements of this system are agents that control traffic signals at intersections. Each agent uses an interval microscopic traffic model to predict effects of its possible control actions in a short time horizon. The executed control action is selected on the basis of predicted delay intervals. Since the prediction results are represented by intervals, the agents can recognize and suspend those control actions, whose positive effect on the performance of traffic control is uncertain. Evaluation of the proposed traffic control system was performed in a simulation environment. The simulation experiments have shown that the proposed approach results in an improved performance, particularly for non-uniform traffic streams.