Abstract. In context-aware route planning, there is a set of transportation agents each with a start and destination location on a shared infrastructure. Each agent wants to find a shortest-time route plan without colliding with any of the other agents, or ending up in a deadlock situation. We present a single-agent route planning algorithm that is both optimal and conflict-free. We also present a set of experiments that compare our algorithm to finding a conflict-free schedule along a fixed path. In particular, we will compare our algorithm to the approach where the shortest conflict-free schedule is chosen along one of k shortest paths. Although neither approach can guarantee optimality with regard to the total set of agent route plans -and indeed examples can be constructed to show that either approach can outperform the other -our experiments show that our approach consistently outperforms fixed-path scheduling.

An important problem in transportation is how to ensure efficient operational route planning when several vehicles share a common road infrastructure with limited capacity. Examples of such a problem are route planning for automated guided vehicles in a terminal and route planning for aircraft taxiing at airports. Maintaining efficiency in such transport planning scenarios can be difficult for at least two reasons. Firstly, when the infrastructure utilization approaches saturation, traffic jams and deadlocks may occur. Secondly, incidents where vehicles break down may seriously reduce the capacity of the infrastructure and thereby affect the efficiency of transportation. In this chapter we describe a new approach to deal with congestion as well as incidents using an intelligent infrastructure. In this approach, infrastructural resources (road sections, crossings) are capable of maintaining reservations of the use of that resource. Based on this infrastructure, we present an efficient, context-aware, operational transportation planning approach. Experimental results show that our context-aware planning approach outperforms a traditional planning technique and provides robustness in the face of incidents, at a level that allows application to real-world transportation problems.

No abstract