Spatial experiences in route finding, such as the ability of finding low‐traffic routes, exert a significant influence on travel time in big cities; therefore, the spatial experiences of seasoned individuals such as taxi drivers in route finding can be useful for improving route‐finding algorithms and preventing using routes having considerable traffic. In this regard, a spatial experience‐based route‐finding algorithm is introduced through ontology in this paper. To this end, different methods of modeling experiences are investigated. Then, a modeling method is chosen for modeling the experiences of drivers for route finding depending on the advantages of ontology, and an ontology based on the taxi drivers’ experiences is proposed. This ontology is employed to create an ontology‐based route‐finding algorithm. The results are compared with those of Google maps in terms of route length and travel time at peak traffic time. According to the results, although the route lengths of route‐finding method based on the ontology of drivers’ experiences in three cases (from nine cases) are greater than that based on Google maps, the travel times are shorter in most cases, and in some routes, the difference in travel time reaches only 10 minutes.

中文翻译：

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基于空间经验的本体发现路线

寻找路线的空间经验，例如发现低流量路线的能力，会对大城市的出行时间产生重大影响；因此，经验丰富的人员（如出租车司机）在寻路中的空间体验对于改进寻路算法和防止使用交通流量大的路线非常有用。在这方面，本文通过本体介绍了一种基于空间经验的路径查找算法。为此，研究了不同的建模经验方法。然后，根据本体的优势，选择一种建模方法来对驾驶员进行路线寻找的体验进行建模，并提出一种基于出租车驾驶员的经验的本体。该本体用于创建基于本体的路由查找算法。将结果与Google地图的结果进行比较，包括路线长度和交通高峰时间的旅行时间。根据结果​​，尽管在三种情况下（从九种情况下）基于驾驶员体验本体的寻路方法的路径长度比基于谷歌地图的寻路方法的路径长度长，但在大多数情况下，行进时间更短，并且有些路线的旅行时间差仅达到10分钟。