Traffic optimization in transport networks based on local routing

TL;DR

This paper presents a cellular automata model for urban traffic flow that employs congestion-aware local routing decisions, demonstrating that local agent-based strategies can optimize overall traffic in real city street networks.

Contribution

It introduces a novel cellular automata model with dynamic, congestion-aware routing for vehicles, showing how local decisions can lead to global traffic improvements.

Findings

Local routing reduces congestion in simulated urban networks

Dynamic updates improve traffic flow efficiency

Model applicable to real city street patterns

Abstract

Congestion in transport networks is a topic of theoretical interest and practical importance. In this paper we study the flow of vehicles in urban street networks. In particular, we use a cellular automata model to simulate the motion of vehicles along streets, coupled with a congestion-aware routing at street crossings. Such routing makes use of the knowledge of agents about traffic in nearby roads and allows the vehicles to dynamically update the routes towards their destinations. By implementing the model in real urban street patterns of various cities, we show that it is possible to achieve a global traffic optimization based on local agent decisions.