Prediction feedback in intelligent traffic systems

TL;DR

This paper investigates how real-time prediction feedback strategies can optimize traffic flow in intelligent systems, demonstrating improved spatial distribution control over traditional feedback methods through cellular automaton simulations.

Contribution

It introduces a prediction feedback strategy for traffic management and shows its superior performance in controlling traffic patterns compared to other feedback methods.

Findings

Prediction feedback improves traffic flow efficiency.

Simulation results outperform traditional feedback strategies.

Effective in managing spatial traffic distribution.

Abstract

The optimal information feedback has a significant effect on many socioeconomic systems like stock market and traffic systems aiming to make full use of resources. In this paper, we studied dynamics of traffic flow with real-time information provided and the influence of a feedback strategy named prediction feedback strategy is introduced, based on a two-route scenario in which dynamic information can be generated and displayed on the board to guide road users to make a choice. Our model incorporates the effects of adaptability into the cellular automaton models of traffic flow and simulation results adopting this optimal information feedback strategy have demonstrated high efficiency in controlling spatial distribution of traffic patterns compared with the other three information feedback strategies, i.e., vehicle number and flux.