Freeway Recurrent Bottleneck Identification Algorithms Considering Detector Data Quality Issues

Abstract

Computer algorithms used to identify recurrent freeway bottlenecks have been studied since the deployment of loop detecting systems. Such algorithms automatically analyze the archived loop detector data and identify potential recurrent bottlenecks and their characteristics, such as location, time of day, and activation rate, for further investigation. In a highway congestion mitigation project, such algorithms can save time and resources for the initial screening of bottlenecks over a large freeway network. These algorithms include rule-based, contour-map-based, and simulation-based methods. However, existing methods require loop detector data with high accuracy and consistency, which is difficult to achieve in prevailing loop detecting systems. This paper proposes a new bottleneck identification algorithm with strong error and noise tolerance. Several simple denoising methods to improve the error resistance of existing algorithms are also proposed. Using statistical error analysis methods, the proposed algorithm and the denoising methods were calibrated and evaluated using field data collected from two distinct freeway corridors (US 12/14 and I-894) in the U.S. state of Wisconsin. Ground truth data for this study come from the manual inspection of 287,055 traffic video snapshots in the course of a month. In the evaluation tests, the proposed algorithm can produce quality congestion identification results with fewer false alarms than the existing algorithms, especially when identifying severe bottleneck congestion.