Context-Aware Path Ranking in Road Networks

Ranking paths becomes an increasingly important functionality in many transportation services, where multiple paths connecting a source-destination pair are offered to drivers. We study ranking such paths under specific contexts. More specifically, we model ranking as a regression problem where we assign a ranking score to each path with the help of historical trajectories. To solve the regression problem, we first propose an effective training data enriching method to obtain a compact and diversified set of training paths using historical trajectories. Next, we propose a multi-task learning framework that considers features representing both candidate paths and contexts. Specifically, a road network embedding is proposed to embed paths into feature vectors by considering both road network topology and spatial properties. By modeling different departure times as a temporal graph, graph embedding is used to embed departure times. The objective function not only considers the discrepancies on ranking scores but also the reconstruction errors of the spatial properties of the paths, which in turn improves the final ranking estimation. Empirical studies on a substantial trajectory data set offer insight into the designed properties of the proposed framework and indicating that it is effective and practical in real world settings.