An Online-Traffic-Prediction Based Route Finding Mechanism for Smart City

Abstract

Finding fastest driving routes is significant for the intelligent transportation system. While predicting the online traffic conditions of road segments entails a variety of challenges, it contributes much to travel time prediction accuracy. In this paper, we propose O-Sense, an innovative online-traffic-prediction based route finding mechanism, which organically utilizes large scale taxi GPS traces and environmental information. O-Sense firstly exploits a deep learning approach to process spatial and temporal taxi GPS traces shown in dynamic patterns. Meanwhile, we model the traffic flow state for a given road segment using a linear-chain conditional random field (CRF), a technique that well forecasts the temporal transformation if provided with further supplementary environmental resources. O-Sense then fuses previously obtained outputs with a dynamic weighted classifier and generates a better traffic condition vector for each road segment at different prediction time. Finally, we perform online route computing to find the fastest path connecting consecutive road segments in the route based on the vectors. Experimental results show that O-Sense can estimate the travel time for driving routes more accurately.