Popular navigation applications and services optimize routes based on either distance or time, disregarding drivers’ preferences when suggesting routes. Various unknown circumstances may affect users’ travel behaviors between two locations on the road network, hence it is complicated to provide satisfactory personalized route recommendations. In this paper, it is believed that users’ travel behaviors are implicitly reflected and can be learned from their historical Global Positioning System (GPS) trajectories. The Behavior-based Route Recommendation (BR²) method is proposed to compute personalized routes based exclusively on users’ travel preferences. The concepts of appearance and transition behaviors are defined to describe users’ travel behaviors. The behaviors are extracted from users’ past travels and the missing behaviors, of unvisited locations, are estimated with the Optimized Random Walk with Restart technique. Furthermore, the temporal dependency of travel behaviors is considered by constructing a time difference interval histogram. A behavior graph is generated to allow the maximum probability route computation with the shortest path algorithm, resulting in the most likely route to be taken by a user. An extension is proposed, named BR²+, to better consider the temporal dependency and incorporate distance in the recommendation process. Experiments conducted on two real GPS trajectory data sets demonstrate the efficiency and effectiveness of the proposed method. In addition, a web-based geographic information system (GIS) called MPR is implemented to demonstrate differences in route recommendation when time, distance, or users’ preferences are considered, besides providing insight about users’ movement through data visualization of their spatial and temporal coverage.

流行的导航应用程序和服务根据距离或时间优化路线，在建议路线时不考虑驾驶员的偏好。各种未知的情况可能会影响用户在路网两个地点之间的出行行为，因此提供令人满意的个性化路线推荐很复杂。在本文中，认为用户的旅行行为被隐含地反映，并且可以从他们的历史全球定位系统（GPS）轨迹中学习。基于行为的路由推荐（BR ²) 方法被提出来完全基于用户的旅行偏好来计算个性化路线。定义了外观和过渡行为的概念来描述用户的旅行行为。行为是从用户过去的旅行中提取的，而未访问位置的缺失行为则通过优化随机游走与重启技术进行估计。此外，通过构建时间差区间直方图来考虑出行行为的时间依赖性。生成行为图以允许使用最短路径算法计算最大概率路线，从而得出用户最有可能采取的路线。提出了一个扩展，命名为 BR ²+，更好地考虑时间依赖性并在推荐过程中加入距离。在两个真实 GPS 轨迹数据集上进行的实验证明了所提出方法的效率和有效性。此外，还实施了一个名为 MPR 的基于 Web 的地理信息系统 (GIS)，以展示考虑时间、距离或用户偏好时路线推荐的差异，此外还通过用户的空间和时间数据可视化提供有关用户移动的洞察。覆盖。