Arterial travel time estimation based on vehicle re-identification using magnetic sensors: Performance analysis

Abstract

Two versions of an arterial travel time estimation method based on vehicle re-identification using wireless magnetic sensors were studied across an arterial segment with multiple intersections. Both methods are based on the same travel time estimation system, but one of them uses the so called original signal processing algorithm while the other one uses a recently modified version of it. Both methods were tested on a 0.51 km (0.32 mile)-long segment of West 34th Street in New York, NY, under harsh driving conditions (i.e. right after a winter storm). The original and modified system results were compared against ground truth data obtained from video. Based on the ground truth data it was possible to determine the travel time distribution and the percentage of vehicles that each of the different methods was able to re-identify. During an analysis period of 45 minutes, 318 vehicles were registered to go across the arterial segment. The original method has a 62% re-identification rate, while the modified method has a 69% rate. Based on comparisons of travel time distribution and empirical cumulative distribution functions, it was observed that the modified method travel time distribution is closely related to the ground truth distribution, while the original method significantly diverges from the ground truth at long travel times.