Improving Freeway Traffic Speed Estimation Using High Resolution Loop Detector Data

Principal Investigator(s):

Henry Liu, Former Professor, Civil, Environmental and Geo-Engineering

Project summary:

This research developed an innovative methodology to solve a long-standing traffic engineering problem--measuring traffic speed using data from single inductive loop detectors. Traditionally, traffic speeds are estimated using aggregated detector data with a manually calibrated effective vehicle length. However, the calibration effort (usually through running probe vehicles) is time consuming and costly. Instead of using aggregated data, the researchers' data collection system records every vehicle-detector actuation "event" so that for each vehicle the researchers can identify the time gap and the detector occupation time. With such high-resolution "event-based" data, the researchers devised a method to differentiate regular cars from longer vehicles. The proposed method is based on the observation that longer vehicles will have longer detector occupation time. Therefore, the researchers can identify longer vehicles by detecting the changes of occupation time in a vehicle platoon. The event-based detector data can be obtained by implementing the SMART-Signal (Systematic Monitoring of Arterial Road Traffic Signals) system, which was developed by the principal investigator and his students over the last five years. The method is tested using the data from Trunk Highway 55, which is a high-speed arterial corridor controlled by coordinated traffic signals. The result shows that the proposed method can correctly identify most of the vehicles passing by inductive loop detectors. The identification of long vehicles will improve the estimation of effective vehicle length on roads. Consequently, speed estimation from the inductive loop detector is improved.


Project details: