Real-time speed and truck data are important inputs for modern freeway traffic control and management systems. However, these data are not directly measurable by single-loop detectors. Although dual-loop detectors provide speeds and classified vehicle volumes, there are too few of them on our current freeway systems to meet the practical needs of advanced traffic management systems. This makes it extremely desirable to develop appropriate algorithms to calculate speed and truck volume from single-loop outputs or from video data. To obtain quality estimates of traffic speed and truck volume data, several algorithms were developed and implemented in this study. These algorithms are (1) a speed estimation algorithm based on the region growing mechanism and single-loop measurements; (2) a set of computer – vision-based algorithms for extracting background images from a video sequence, detecting the presence of vehicles, identifying and removing shadows, and calculating pixel-based vehicle lengths for classification; and (3) a speed estimation algorithm that uses paired video and singleloop sensor inputs. These algorithms were implemented in three distinct computer applications. Field-collected video and loop detector data were used to test the algorithms. Our test results indicated that quality speed and truck volume data can be estimated with the proposed algorithms by using single-loop data, video data, or both video and single-loop data. The Video-based Vehicle Detection and Classification (VVDC) system, based on the proposed video image processing algorithms, provides a cost-effective solution for automatic traffic data collection with surveillance video cameras. For locations with both video and singleloop sensors, speed estimates can be improved by combining video data with single-loop data. (Author/publisher)
Abstract