Maximum-likelihood speed estimation using vehicle-induced magnetic signatures

Modern traffic management systems require accurate vehicle detection, speed estimates, and link travel times for congestion detection, traveler information, ramp metering, optimization of traffic signal timing, and planning. Current speed estimation methods report speeds that are averaged over at least 30 seconds. This is necessary in some cases because the estimates tend to be noisy or in other cases because the algorithms are not intended to deliver individual vehicle speeds. This paper develops an algorithm based on communication theory and compares the results to conventional algorithms. The maximum-likelihood algorithm proposed in this paper provides significantly improved speed estimates that can be used to produce histograms of vehicle speeds instead of the speed averages currently available.