Modeling of Traffic-Aware Travel Time in Spatial Networks

Route evaluation and optimization have received significant attention in recent years. In this light, we propose and investigate a novel problem of estimating the travel time (TTE query) for a user specified path by considering the related traffic conditions. Given a query path and a departure time, TTE query finds the estimated travel time along this path. We believe that this type of query may bring important benefits to users in many popular applications, such as travel route evaluation and optimization, and route planning and recommendation. To address the TTE problem in a convincing approach, we construct a traffic-aware spatial network Gpt(V, E) by analysing uncertain trajectory data of moving objects. Based on Gpt(V, E), we define two novel types of TTE queries: TTEep for an exact query path, and TTElp for a loose query path. The performance of the construction of traffic-aware spatial network is verified by extensive experiments based on real and synthetic spatial data sets.