Abstract. The global growth in urbanisation increases the demand for services including road transport infrastructure, presenting challenges in terms of mobility. These trends are occurring in the context of concerns around environmental issues of poor air quality and transport related carbon dioxide emissions. One out of several ways to help meet these challenges is in the intelligent routing of road traffic through congested urban areas.Our goal is to show the feasibility of using automated planning to perform this routing, taking into account a knowledge of vehicle types, vehicle emissions, route maps, air quality zones, etc. Specifically focusing on air quality concerns, in this paper we investigate the problem where the goals are to minimise overall vehicle delay while utilising network capacity fully, and respecting air quality limits. We introduce an automated planning approach for the routing of traffic to address these areas. The approach has been evaluated on micro-simulation models that use real-world data supplied by our industrial partner. Results show the feasibility of using AI planning technology to deliver efficient routes for vehicles that avoid the breaking of air quality limits, and that balance traffic flow through the network.

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The local ramp metering strategy ALINEA aims at stabilizing downstream motorway occupancy near critical occupancy to achieve maximum motorway throughput. Two previously proposed real-time estimation schemes for the critical occupancy are fine-tuned, modified, and evaluated by use of extensive traffic data from eight locations of the UK M6 motorway. The evaluation results indicate satisfactory estimation accuracy, rapid tracking behavior, and high robustness for measurement noise, detector failures, and different kinds of traffic conditions. The reported investigations open the way to field implementation of the adaptive AD-ALINEA ramp metering strategy that incorporates real-time estimation of critical occupancy.

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