Towards fine-grained urban traffic knowledge extraction using mobile sensing

We introduce our vision for mining fine-grained urban traf-fic knowledge from mobile sensing, especially GPS location traces. Beyond characterizing human mobility patterns and measuring tra±c congestion, we show how mobile sensing can also reveal details such as intersection performance statis-tics that are useful for optimizing the timing of a tra±c sig-nal. Realizing such applications requires co-designing pri-vacy protection algorithms and novel tra±c modeling tech-niques so that the needs for privacy preserving and tra±c modeling can be simultaneously satisfied. We explore pri-vacy algorithms based on the virtual trip lines (VTL) con-cept to regulate where and when the mobile data should be collected. The tra±c modeling techniques feature an inte-gration of tra±c principles and learning/optimization tech-niques. The proposed methods are illustrated using two case studies for extracting tra±c knowledge for urban signalized intersection