An Approach for Detecting Data Anomalies at Permanent Cycling Count Stations
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 1
Abstract
With 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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Data Availability Statement
Some or all data, models, or code used during the study were provided by a third party. Direct requests for these materials may be made to the provider as indicated in the Acknowledgments.
Acknowledgments
The author would like to thank the City of Vancouver and Dr. Mohamed Eisa for providing the data used in this study.
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© 2022 American Society of Civil Engineers.
History
Received: Aug 25, 2021
Accepted: Sep 9, 2022
Published online: Nov 10, 2022
Published in print: Jan 1, 2023
Discussion open until: Apr 10, 2023
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Cited by
- Greg Lindsey, Sebastian Coll, Gustave Stewart, Quality Assurance Methods for Hourly Nonmotorized Traffic Counts, Transportation Research Record: Journal of the Transportation Research Board, 10.1177/03611981231175898, 2678, 2, (723-742), (2023).