Urban traffic analysis through multi-modal sensing

This paper makes contributions toward adopting a systemic view of city-wide ubiquitous systems. Here, we present methods and techniques for combining multiple sensing modalities to measure and model traffic patterns in urban environments. We show how noise in one modality can be reduced by considering another more reliable modality and how two modalities can be combined. While much work in the literature deals with simulated data or small data sets, our work focuses on analyzing data from a permanent data collection infrastructure in a downtown area. We present results using a 3-week data set containing data of two modalities: inductive loop traffic detectors and Bluetooth scanners.