Using Dual-Loop Event Data to Enhance Truck Data Accuracy

Abstract

The economic vitality of Washington State depends on continuing and even increasing strong foreign trade levels. Improving the mobility of freight traffic on Washington's streets and highways is an important factor in achieving this goal. The Washington State Department of Transportation (Washington State DOT) has devoted significant time and resources to investigating ways to improve freight mobility on its freeway networks. The primary type of sensor used by the department to collect truck volume and speed data on Washington State freeways is a dual-loop detection system. However, recent studies of the Washington DOT classification system found that the quality of dual-loop truck data being collected was questionable because of four main problems: (a) split of loop signals for multiunit trucks, (b) cross talk between adjacent loops, (c) constrained sensitivity adjustments of discrete levels at loop amplifiers, and (d) unsuitable thresholds of on-time differences between the two single loops of a dual-loop detector. This paper presents an algorithm for minimizing the effects of these four problems. The algorithm uses traffic information extracted from loop event data and the characteristics of vehicle length distribution to identify and correct erroneous loop event data for better truck data extraction. The proposed algorithm does not require hardware installations or adjustments to address the four problems affecting the quality of truck data obtained from dual-loop detectors. Therefore, it provides a cost-effective solution for obtaining much more accurate truck data from this commonly available detection system.