Kalman Filter Approach to Speed Estimation Using Single Loop Detector Measurements under Congested Conditions

The ability to measure or estimate accurate speed data are of great importance to a large number of transportation system operations applications. Estimating speeds from the widely used single inductive loop sensor has been a difficult, yet important challenge for transportation engineers. Based on empirical evidence observed from sensor data collected in two metropolitan regions in Virginia and California, this research developed a Kalman filter model to perform speed estimation for congested traffic. Taking advantage of the coexistence of dual loop and single loop stations in many freeway management systems, a calibration procedure was developed to seed and initiate the algorithm. Finally, the paper presents an evaluation that illustrates that the proposed algorithm can produce acceptable speed estimates under congested traffic conditions, consistently outperforming the conventional $g$ -factor approach.