An Automated Process for Identification of Bottlenecks in the Traffic System Using Large Data Sets
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 3
Abstract
Traffic breakdowns are frequently observed phenomena on roads in larger cities, especially during peak hours. Locations along a road stretch with frequently observed breakdowns are known as recurrent bottlenecks. Knowledge about bottleneck locations are important for improvement of traffic conditions at these locations. Bottleneck locations can be identified through manual inspection of data. However, due to the comprehensive amount of data that are available today, it becomes impractical to manually identify breakdowns and instead, an automated process can be used. We propose such an automated method. The proposed method is applied to a use case south of Stockholm in Sweden. One month of data collected at densely spaced detectors is used to investigate the sensitivity of the parameter settings. After calibration of the threshold values, 100% of the larger breakdowns and 40% of the medium size breakdowns are identified. Smaller breakdowns, not giving significant impact on the traffic conditions, are only detected in 10%–20% of the cases. Thereafter, the method is applied to 1 year of data to illustrate the applicability of the method on a larger data set. The results show that the method is promising to use for identification of recurrent bottleneck locations.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request; and 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
This work was supported by the Swedish Transport Administration (Trafikverket) through the Centre for Traffic Research (CTR) under Grant TRV 2017/68538.
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© 2022 American Society of Civil Engineers.
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Received: Dec 12, 2021
Accepted: Nov 7, 2022
Published online: Dec 29, 2022
Published in print: Mar 1, 2023
Discussion open until: May 29, 2023
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