Multi-Sensor Data Fusion for Accurate Traffic Speed and Travel Time Reconstruction

This paper studies the joint reconstruction of traffic speeds and travel times by fusing sparse sensor data. Raw speed data from inductive loop detectors floating cars as well time measurements are combined using different fusion techniques. A novel approach is developed, which extends existing methods to integrate low-resolution Several state-of-the-art evaluated on their performance in reconstructing various combinations Algorithms setups with real detector, car Bluetooth collected during severe congestion German freeway A9. Two main aspects examined: 1) algorithm provides most accurate result depending used 2) type combination sensors yields highest estimation accuracy. Results show that, overall, applied a floating-car best Furthermore, sources improves quality many, but not all cases. In particular, only provide benefit for purposes if integrated subtly.