Traffic Data Measurement and Validation

Benjamin Coifman

Institute of Transportation Studies, Research Reports, Working Papers, Proceedings from Institute of Transportation Studies, UC Berkeley

Abstract: Caltrans collects traffic data for many monitoring and control applications and the ultimate goal of the traffic surveillance system is to provide accurate data to these high level applications. The surveillance system includes data measurement, averaging and verification algorithms. This report presents improvements to many elements of the surveillance system. First, section 2addresses many shortcomings in average speed estimation at single loop detectors, as well as other sensors that estimate speed from average flow and occupancy. At the root of these problems is the fact that the conventional estimation methodology assumes a fixed vehicle length. It is shown that this assumption does not hold for many samples, both because the true average vehicle length can change throughout the day and because a given sample may not be representative of an average sample. Next, section 3 presents a more accurate method to estimate velocity at single loop detectors. It is shown that this method approaches the accuracy of velocity measurements from dual loop detectors. This new approach does not eliminate the benefit of dual loops, section 4 presents a new method to estimate link travel time from measurements recorded at a dual loop detector. The estimates are very close to the true travel times and it is shown that when estimation errors do occur, they can usually be identified. Finally, experience by Caltrans shows that there is a need to develop and deploy more sophisticated error detection and data verification algorithms. Section 5 presents eight new detector validation tests using data on individual vehicles, i.e., event data.

Keywords: Engineering (search for similar items in EconPapers)
Date: 2001-12-01
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