文摘
Map-matching is a hot research topic as it is essential for Moving Object Database and Intelligent Transport Systems. However, existing map-matching techniques cannot satisfy the increasing requirement of applications with massive trajectory data, e.g., traffic flow analysis and route planning. To handle this problem, we propose an efficient map-matching algorithm called Passby. Instead of matching every single GPS point, we concentrate on those close to intersections and avoid the computation of map-matching on intermediate GPS points. Meanwhile, this efficient method also increases the uncertainty for determining the real route of the moving object due to less availability of trajectory information. To provide accurate matching results in ambiguous situations, e.g., road intersections and parallel paths, we further propose Passby*. It is based on the multi-hypothesis technique and manages to maintain a small but complete set of possible solutions and eventually choose the one with the highest probability. The results of experiments performed on real datasets demonstrate that Passby* is efficient while maintaining the high accuracy.