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A Review of Stop-and-Go Traffic Wave Suppression Strategies: Variable Speed Limit Versus Jam-Absorption Driving

Zhengbing He et al.

IEEE Transactions on Intelligent Transportation Systems2026https://doi.org/10.1109/tits.2026.3658644article
ABDC A
Weight
0.37

Abstract

The main form of freeway traffic congestion is the familiar stop-and-go wave, characterized by wide moving jams that propagate indefinitely upstream provided enough traffic demand. They cause severe, long-lasting adverse effects, such as reduced traffic efficiency, increased driving risks, and higher vehicle emissions. This underscores the crucial importance of artificial intervention in the propagation of stop-and-go waves. Over the past two decades, two prominent strategies for stop-and-go wave suppression have emerged: variable speed limit (VSL) and jam-absorption driving (JAD). Although they share similar research motivations, objectives, and theoretical foundations, the development of these strategies has remained relatively disconnected. To synthesize fragmented advances and drive the field forward, this paper first provides a comprehensive review of the achievements in the stop-and-go wave suppression-oriented VSL and JAD, respectively. It then focuses on bridging the two areas and identifying research opportunities from the following perspectives: fundamental diagrams, secondary waves, generalizability, traffic state estimation and prediction, robustness to randomness, simulation scenarios for strategy validation, and field tests and practical deployment. We expect that through this review, one area can effectively address its limitations by identifying and leveraging the strengths of the other, thus promoting the overall research goal of freeway stop-and-go wave suppression.

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https://doi.org/https://doi.org/10.1109/tits.2026.3658644

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@article{zhengbing2026,
  title        = {{A Review of Stop-and-Go Traffic Wave Suppression Strategies: Variable Speed Limit Versus Jam-Absorption Driving}},
  author       = {Zhengbing He et al.},
  journal      = {IEEE Transactions on Intelligent Transportation Systems},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1109/tits.2026.3658644},
}

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.