Context-dependent Route Generation Scheme Using Network Voronoi Diagrams

Abstract

Researches on computing an optimal route according to user's preference or spatial contexts have been gaining focus recently, as car navigation and pedestrian navigation systems are getting accustomed to be used in daily lives. In this paper, the authors propose a method to compute a path to pass through as many context areas as possible along its route, according to the user's needs. Specifically, the authors apply the concept to construct a Delaunay Diagram from neighbor nodes in Voronoi Diagram, and use Network Voronoi Diagram to construct a subgraph based on contexts. For route search, the authors use A* shortest path algorithm over the subgraph. To verify the efficiency of the proposed method with subgraphs, the authors perform route search over subgraphs and base graph, which was generated from the road network of the real space, and compare the results. The results show that the route search by the proposed method consist of more paths with context areas than those performed over the base graph. Moreover, the proposed method reduces the number of steps for path search.

Context-aware abstraction and generalization of street networks: two cognitively engineered user-oriented approaches using network Voronoi diagrams

“…Approach 1: model generalization of street network data set for optimal route finding This section describes the method of generalizing street networks to find an optimal route. The proposed method extracts a sub-network by model generalization of a street network data set using network Voronoi diagrams to make the sub-network satisfy the criteria defined by the user based on Kambara et al (2007). This approach emphasizes the preferred edges of the user and uses them in the optimal route.…”

“…Parent node A of node B is the node that is a minimum distance to generator point associated with B among the neighbour nodes of B. This means that generator point A is the parent of B in the shortest path tree rooted at S (Kambara et al 2007). In the network Voronoi diagram shown in Figure 3, node S is the generator point of nodes J1 and J2 and edge E1.…”

A route navigation algorithm for pedestrian simulation based on grid potential field

“…As to the spatial map representation modeling, the visibility graph, [12][13][14] the Voronoi diagram, [15][16][17][18][19][20] and the navigation mesh [21][22][23][24] serve as the widely used map generation modeling method. In the visibility graph, the initial position, the goal position, and the vertices of obstacles in the simulation scenario are represented by the nodes.…”

“…Unlike the visibility graph, the nodes of the spatial map are the vertices of polygons, and the edges of the map are the sides of polygons constructed using equidistant points from the adjacent two points on the obstacle's boundaries. Kambara et al 15 applied the concept to construct a Delaunay diagram from neighbor nodes in the Voronoi diagram and used network Voronoi diagram to construct a subgraph based on the context for pedestrian route search. Nakamura et al 16 has given a pedestrian movement model based on the Voronoi diagram, by which the walking route and direction of each pedestrian can be determined.…”