Investigation of Speed Estimation Using Single Loop Detectors

摘要

The ability to collect or estimate accurate speed information is of great importance to a large number of Intelligent Transportation Systems (ITS) 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 the sensor data from two metropolitan regions in Northern Virginia and California, this research effort 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 typical freeway management systems, a calibration procedure was proposed for seeding and initiating the algorithm. Empirical evaluation showed that the proposed algorithm can produce accurate speed estimates (on the order of 1-3 miles/hour error) under congested traffic conditions.