A Collaborative Method for Route Discovery Using Taxi Drivers’ Experience and Preferences

Abstract

This paper presents a collaborative route discovery method that leverages the experience and preferences of taxi drivers in urban areas. The proposed method is mainly comprised of two phases: collaborative preference discovery (CPD) and intelligent driver network generation (IDNG). In the first phase, given an origin-destination (O-D) pair and provided that the cluster is a road segment set within a time-reachable range, we propose CPD which involves cluster-to-cluster retrieval to capture the top-k routes that are not only frequently traversed by taxis but also neighboring to the O-D pair. In the second phase, to support route computation, an IDNG algorithm is devised to generate an experiential graph for each specific O-D pair. In empirical studies, using the period-based experiential route database, sensitivity analysis is employed to select optimal parameters of intelligent driver networks. The results demonstrate that the routes recommended by our collaborative method are much more reliable than those of the shortest-path method with respect to the variance of travel time. Moreover, the recommended routes are traversed more frequently than those of the fastest-path and the shortest-path methods, while the travel time and route lengths of our routes are approximately equal to those of the conventional methods.