Context-Aware Route Planning

Abstract

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.

Conflict‐free time‐based trajectory planning for aircraft taxi automation with refined taxiway modeling

“…In this section, we compare the DRT algorithm with two sequential planning methods presented in related researches [25,36,44] to show its need and advantages.…”

“…In order to provide conflict-free taxi trajectories, we first discuss the conditions for taxi conflict avoidance and set up corresponding zone control rules to meet these conditions using time window-related concepts. Then a dynamic routing and timing algorithm is formulated accordingly to find out conflict-free taxi trajectories for a newly joined aircraft, which is a direct extension of the method presented in [36]. The presented algorithm can be naturally integrated with the sequential planning framework, which shows potential for realistic surface trajectory planning [25].…”

“…Furthermore, if the heuristic is consistent, the presented algorithm can find out the existing shortest time conflict-free trajectory in an optimal way in the sense that no other admissible algorithm could expand fewer times [42,43]. Now we briefly discuss the considerations for formulating the algorithm in the aforementioned way: First, in a related study by ter Mors et al [36], a free time window graph is constructed before the route planning process: Each free time window of a resource (similar to the zone concept of this paper) is modeled as a vertex of the graph, then arcs of the graph are defined to represent the temporal connectivity between free time windows of spatially connected resources. However, as mentioned by the authors, accessibility to the free time windows of neighboring resources still need to be examined during each expansion, because the existence of an arc between two free time windows cannot ensure valid accessibility to the related neighboring resource; the accessibility also depends on the actual exittime from previous resource.…”

“…Another disadvantage of the approach presented in [36] is the undiscriminating treatment of lanes and intersections during the search process. According to our earlier analysis, for a given temporal node, there may only be one enterable temporal node related to a neighboring lane, while there may be more than one enterable temporal node related to a neighboring intersection.…”

Modeling of decentralized processes in dynamic logistic networks by means of graph-transformational swarms

“…One of the important problems that a designer of an AGV system faces in complex areas is the collision-free routing problem. The classical way to solve this problem is the central time windows planning (see, e.g., [22,24,25]). However, the tendency in the last years is to explore more decentralized approaches (e.g., [21,27]).…”