Towards Fine-Grained Urban Traffic Knowledge Extraction Using Mobile Sensing
The authors introduce their vision for mining fine-grained urban traffic knowledge from mobile sensing, especially GPS location traces. Beyond characterizing human mobility patterns and measuring traffic congestion, they show how mobile sensing can also reveal details such as intersection performance statistics that are useful for optimizing the timing of a traffic signal. Realizing such applications requires co-designing privacy protection algorithms and novel traffic modeling techniques so that the needs for privacy preserving and traffic modeling can be simultaneously satisfied. They explore privacy algorithms based on the Virtual Trip Lines (VTL) concept to regulate where and when the mobile data should be collected.