A Hybrid Automated Planning Approach for Urban Real-time Routing of Connected Vehicles

One of the pivotal challenges presented to urban road traffic controllers is the effective utilisation of transport infrastructure, as a result of growing urbanisation, the finite network capacity, and of the increasing number of road vehicles. The arrival of connected autonomous vehicles (CAVs) represents a unique opportunity for a fundamental change in urban mobility, and urban traffic control should take an active role in integrating CAVs into the mobility ecosystem in order to maximise benefits. A practical way to exploit CAVs for urban traffic control is to build an intelligent control mechanism that can distribute traffic in the controlled region. In this context, automated planning, a well-studied Artificial Intelligence topic, can provide techniques to dynamically generate plans to distribute traffic –thus maximising the network utilisation and reduce congestion. In this paper, we present an approach based on hybrid discrete continuous planning, and we demonstrate its impact using real-world historical data of a large UK town.