Estimating mobility distributions from uncertain roadside sensor datasets

Chrysovalantis Anastasiou, John Krumm, Cyrus Shahabi

Abstract: Understanding human mobility patterns is crucial for urban planning, resource allocation, and personalized recommendations. However, real-world trajectory data are rarely released publicly due to privacy concerns. At the same time, metropolitan cities are becoming equipped with various roadside sensors, such as CCTV cameras and RFIDs. Unlike trajectory data, these sensors do not uniquely identify and track vehicles, making extracting mobility patterns from their detections challenging. In this paper, we propose VPE, a framework that processes roadside sensor observations to estimate the probability that a vehicle visits a road segment at a certain time. At the core of VPE, we implement LEM, a novel mathematical model that calculates location transition probabilities taking into account the sensors’ reliability. Lastly, we propose APD+, an algorithm that captures the uncertainty of movement between two endpoints. Our experiments show that the proposed methods achieve high accuracy while maintaining practical computation time.