Blind Deconvolution of Vehicle Inductive Signatures for Travel Time Estimation

Travel-time data provides vital information for traffic monitoring, management, and planning. The objective of this research was to develop a new computational approach that could accurately measure travel time from two sets of spatially separated loop detectors using re-identification of vehicle inductance signatures generated by the loops. Although measuring travel time using loop inductance signatures is not new, all past approaches essentially relied on pattern matching of raw inductance waveforms without restoring the loss of detailed features caused by a large detection zone of inductive loops. The main effort in this research was to develop a new computational algorithm that restores the lost details from the raw inductance waveforms by modeling the output of loop detectors as a convolution of the original vehicle signature and the loop system function. This restoration problem was formulated as a blind deconvolution problem since we know neither the impulse response of the loop detectors, nor the original vehicle signature. To solve this blind problem, two basic blind deconvolution approaches were used, Godard deconvolution and constrained least squares. Experimental results showed that both methods performed well and significantly exposed the original signature information with unique vehicle characteristics. The author and the Minnesota Department of Transportation and/or the Center for Transportation Studies do not endorse products or manufacturers. Trade or manufacturers' names appear herein solely because they are considered essential to this report. Traffic Management Center (RTMC) served as the technical liaison for this project and organized all of the Technical Advisory Panel (TAP) meetings and supervised the project progress. Dr. Eil Kwon and Doug Lau at Mn/DOT RTMC participated in many of the TAP meetings and provided thoughtful inputs. Anushiri Parsekar, University of Minnesota Duluth (UMD) Computer Science (CS) graduate student, worked as a research assistant and completed her MS thesis under this project. She contributed in coding the algorithm and analyzing the data for the project. Jeanne Hartwick at the Northland Advanced Transportation Systems Research Laboratories (NATSRL) helped with all accounting and purchase related matters. Carol Wolosz at NATSRL, who works as the program coordinator, always brought good spirits to all of my transportation research projects. I thank both for their support. Dave Keranen at NATSRL helped to obtain inductance signatures for various speeds using his truck and also volunteered to build a prototype impulse input device for testing the loop characteristic function. Earl Hoekman and George Coffee at the 3M Traffic Control Division, …