Signal processing methods for road condition monitoring and vehicle signature matching for travel time estimation
Abstract
This thesis develops the signal processing methods needed for (1) a low-cost distributed pavement monitoring system, and for (2) travel time estimation using vehicle re-identification via signature matching. (1) A signal processing approach is proposed for jointly filtering and fusing multiple spatially-indexed measurements captured by a fleet of vehicles. It is assumed that these measurements are corrupted by sensor noise in addition to registration errors induced by GPS positioning uncertainties. Measurements from low-cost vehicle mounted-sensors (e.g., accelerometers and standard GPS devices) are properly combined to synthesize higher quality road roughness data for cost-effective distributed road condition monitoring. The proposed algorithms are implementable in practice; they are recursive and thus require only moderate computational power and memory space. The results of this work are important for future roadway management systems which will use on-road vehicles as a distributed network of sensing probes gathering spatially-indexed measurements for environmental sensing and/or traffic monitoring. The methods and algorithms are tested using real data. (2) Link travel times are crucial information for advanced traveler information and traffic management applications. However, current systems for estimating them have many shortcomings. A novel signal processing framework for travel time estimation using vehicle re-identification via signature-matching is proposed. Individual vehicles are matched between well-separated stations in a road transportation network using vehicle electromagnetic signatures captured by roadway embedded sensors. Statistical signal processing techniques are used to develop effective signature pre-processing algorithms that support the subsequent signature matching problem, which is formulated using techniques from communication theory. A probabilistic modeling of the observed matching assignments is used to devise a travel time estimation strategy that is robust to potential vehicle misidentifications. The proposed techniques are tested under real traffic scenarios and accurate link travel times are reported.
Degree
Ph.D.
Advisors
Bullock, Purdue University.
Subject Area
Civil engineering|Electrical engineering
Off-Campus Purdue Users:
To access this dissertation, please log in to our
proxy server.