Abstract
Rapidly increasing volumes of GPS data collected from vehicles provide new and increasingly comprehensive insight into the routes that drivers prefer. While routing services generally compute shortest or fastest routes, recent studies suggest that local drivers often prefer routes that are neither shortest nor fastest, indicating that drivers value route properties that are diverse and hard to quantify or even identify. We propose a routing service that uses an existing routing service while exploiting the availability of historical route usage data from local drivers. Given a source and destination, the service recommends a corresponding route that is most preferred by local drivers. It uses a route preference function that takes into account the number of distinct drivers and the number of trips associated with a route, as well as temporal aspects of the trips. The paper provides empirical studies with real route usage data and an existing online routing service.