Self-adaptive Traffic and Logistics Flow Control using Learning Agents and Ubiquitous Sensors

Abstract

Traffic flow optimisation is a distributed complex problem. Traditional traffic and logistics flow control algorithms operate on a system level and address mostly switching cycle adaptation of traffic signals and lights. This work addresses traffic flow optimisation by self-adaptive micro-level control by combining Reinforcement Learning and rule-based agent models for action selection with a new hybrid agent architecture. I.e., long-range routing is performed by agents that adapt their decision making for re-routing on local environmental sensors. Agent-based modelling and simulation are used to study emergence effects on urban city traffic flows with learning agents. The approach and the proposed agent architecture can be generalised and applied to a broader range of application fields, e.g., logistics and general transport phenomena.