Self-organising Urban Traffic control on micro-level using Reinforcement Learning and Agent-based Modelling

TL;DR

This paper proposes a self-organising traffic control system using reinforcement learning and agent-based modelling, enabling vehicles to adaptively re-route for improved urban mobility.

Contribution

It introduces a novel micro-level control approach combining reinforcement learning with agent-based models for urban traffic management.

Findings

Reinforcement learning enables vehicles to re-route based on local sensors.

Micro-level control improves travel time and path efficiency.

Agent-based simulation demonstrates emergent traffic flow improvements.

Abstract

Most traffic flow control algorithms address switching cycle adaptation of traffic signals and lights. This work addresses traffic flow optimisation by self-organising micro-level control combining Reinforcement Learning and rule-based agents for action selection performing long-range navigation in urban environments. I.e., vehicles represented by agents adapt their decision making for re-routing based on local environmental sensors. Agent-based modelling and simulation is used to study emergence effects on urban city traffic flows. An unified agent programming model enables simulation and distributed data processing with possible incorporation of crowd sensing tasks used as an additional sensor data base. Results from an agent-based simulation of an artificial urban area show that the deployment of micro-level vehicle navigation control just by learned individual decision making and re-routing based on local environmental sensors can increase the efficiency of mobility in terms of path length and travelling time.