AbstractWith the large amounts of available traffic data, it becomes necessary to develop tools that can perform several tasks related to the collected data. These tasks include storing the data in a standard format, filtering the data/flagging suspicious records, processing the data and calculating useful quantitative traffic indices, and finally, visualizing the outcomes. In this paper, a data-driven, yet novel, data-filtering approach was proposed to flag outliers in daily cycling counts at automatic traffic counters (ATCs). The approach was motivated by the spatiotemporal relationship of cycling counts collected at permanent count stations. The proposed approach is flexible because it assumes no prior knowledge about which locations may experience sensor malfunction (i.e., outliers). The approach was tested using a large data set of more than 111,000 daily bicycle volumes collected in 4 years (2016–2019) at more than 60 different permanent count stations in the City of Vancouver, Canada. The approach was validated using complete annual sets of data at four count stations in 2016. Scenarios of undercounting and overcounting were simulated using different percentages of the actual counts. The results showed that the proposed approach has a strong ability in detecting and removing most outliers, especially for cases of substantial undercounting.

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