Shockwaves detection based on inductive loop data: a microscopic simulation-based analysis

Abu Saq, Hassan, Kaparias, Ioannis and Waterson, Ben (2024) Shockwaves detection based on inductive loop data: a microscopic simulation-based analysis. 103rd Transportation Research Board Annual Meeting, Walter E. Washington Convention Center, Washington, United States. 07 - 11 Jan 2024.

Abstract

The early mitigation of shockwaves holds promise for improving traffic flow on freeways. Current research focuses on rapid response strategies to mitigate the effects of shockwaves as they form. However, existing shockwave detection methods often oversimplify the characteristics of shockwaves and vehicle status, limiting their effectiveness. To implement advanced congestion mitigation in real-world scenarios, accurate and efficient shockwave detection algorithms operating at a microscopic level are crucial. This study presents a novel shockwave detection algorithm that utilizes individual vehicles' speeds and headways, which can be obtained from inductive loops. The algorithm introduces a new parameter called "Event Number," reflecting the number of vehicles involved at the early stages of a shockwave. This parameter allows fine-tuning of the notification mechanism, enabling detection of larger shockwaves with significant speed reductions that could cause severe congestion. Moreover, the study develops prediction models to forecast the initial shockwave endpoints and their changes concerning each additional vehicle. The algorithm and prediction models are tested under various scenarios using the PTV VISSIM stochastic microscopic traffic simulation tool on a conceptual freeway stretch where shockwaves occur randomly. The results demonstrate the effectiveness of the proposed algorithm in detecting shockwaves in real-time and facilitating timely preventive action. Overall, this research highlights the potential of early shockwave mitigation and the importance of accurate detection algorithms in managing traffic congestion effectively. The prediction models offer promising insights into forecasting shockwave behaviour, paving the way for improved traffic management strategies on freeways.

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Contributors

Author: Hassan Abu Saq

Author: Ioannis Kaparias

Author: Ben Waterson

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