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With many loop detectors, numerous junctions, and site-specific configuration files, at a freeway detector station there are many opportunities for errors to occur in the mapping from a loop detector input to its physical location and lane in the freeway. If these configuration errors are not caught they propagate to the station's data and any control decisions based on those data. Great care is necessary to prevent such errors, yet they persist in the field even after the added expense to prevent them. This paper develops and tests two algorithms to identify such errors, without using a priori knowledge about the detector station configuration. By correlating events between two loop detectors, the first algorithm tests all active detectors to both match pairs from dual-loop detectors and identify any single-loop detectors at the station. The second algorithm uses a hypothesis-test based K-means method to group lanes from common directions based on time series velocity. Both algorithms exhibited very good performance, as shown herein, they were tested over many days and detector stations. Although the analysis focuses on loop detectors, the methodology should also apply to emerging technologies that mimic loop detectors, e.g., wayside mounted microwave radar.

Particle filtering, also known as sequential Monte Carlo (SMC) sampling, has been successfully applied to general state−space models for Bayesian inference. Being a simulation method, its performance relies to some extent on the generated samples or particles. For a poor initial guess a large fraction of particles is usually less representative of the underlying state’s distribution and could even cause SMC to diverge. In this paper, an intuitive statistic, predictive density is proposed to monitor the particles’ performance. When below a statistically controlled threshold value, our approach triggers smoothing for obtaining a better estimate of the initial state in the case of a poor prior. We find that combining a moving horizon smoother with SMC is very effective for recovering from a poor prior and develop an integrated practical approach that combines these two powerful tools.

With several loop detectors, numerous junctions, and site-specific configuration files, many opportunities exist for errors at a freeway detector station in mapping from a loop detector input to its physical location and lane in the freeway. If these configuration errors are not caught, they propagate to the station's data and any control decisions based on those data. Great care is necessary to prevent such errors, yet they persist in the field even with the added expense of trying to prevent them. This paper develops and tests two algorithms to identify such errors without using a priori knowledge about the detector station configuration. By correlating events between two loop detectors, the first algorithm tests all active detectors to match pairs from dual-loop detectors and to identify any single-loop detectors at the station. The second algorithm uses a hypothesis-test-based K-means method to group lanes from common directions based on time series velocity. Both algorithms exhibited good performance; they were tested over many days and detector stations. Although the analysis focuses on loop detectors, the methodology should also apply to emerging technologies that mimic loop detectors, such as wayside-mounted microwave radar.