Vehicle Re-Identification and Travel Time Measurement in Real-Time on Freeways Using Existing Loop Detector Infrastructure

Abstract

A new vehicle re-identification algorithm for two consecutive detector stations on a freeway, whereby a vehicle measurement made at the downstream detector station is matched with the vehicle’s corresponding measurement at the upstream station, is presented in this paper. The method is illustrated using effective vehicle length measured at dual-loop speed traps, but it is transferable to other detectors capable of extracting a vehicle signature (such as video image processing). This approach is significant because no one has attempted to use the existing detector infrastructure to match vehicle measurements between detector stations. The algorithm should improve freeway surveillance via travel time measurement, which is simply the difference between the known arrival times at the two stations for a matched vehicle. The re-identification algorithm is tolerant to noise; instead of finding the ‘best match’ for each vehicle, it finds all possible matches and then looks for sequences of vehicles from the possible matches. Even with noisy loop detector data, the sequence detection eliminates most of the possible-but-incorrect matches while the true matches remain. The new methodology will be used to examine the applications and benefits of travel-time data on real-world traffic, without the expensive costs of installing new detectors. Ordinarily, a travel-time measurement system would have to be fully deployed before the benefits can be quantified.