Optimal Sequential Merging Strategy Based on Adaptive Threshold for Ramp Traffic Involving Platoons
The platoon technology is gradually applied in the intelligent transportation systems to improve the safety, efficiency, energy saving, and emission reduction of vehicles during driving. However, in contrast to the traditional ramp merging scenario that consists of individual vehicles, the existing platoons from the upstream bring new challenges to the merging problem on ramps. Without an effective coordination strategy, the unnecessary congestion and disintegration of the existing platoons will lead to an increase in driving costs and safety risk. Therefore, this paper optimizes the merging strategy of vehicle sequences involving platoons on highway ramps. A merging strategy, based on an adaptive headway threshold, is first proposed, where the comprehensive economic benefit is considered as its optimization objective while taking into account the decrements of the time, fuel, and carbon emission costs. The vehicle sequence merging is then modeled as a Markov decision process to derive the optimal threshold, where the action set includes three actions: accelerating, decelerating, and maintaining the constant cruising speed. Afterwards, the constraints are brought to the decision-making to ensure the safety of the merging process. The calculation of the action value, combining the derived optimal threshold and constraint, is then detailed. Finally, the results of the simulation are evaluated, and a comparison between the proposed strategy and other existing methods is conducted, which demonstrates that the proposed approach improves the comprehensive economic benefits.
Authors
- Yun Meng (ORCID: https://orcid.org/0000-0002-2317-9379)
- Panpan Yang (ORCID: https://orcid.org/0000-0002-4450-9453)
- Ping Wang (ORCID: https://orcid.org/0000-0003-2963-9476)
- Yinli Jin (ORCID: https://orcid.org/0000-0002-2803-8989)
- Shengfang Liao
Institutions
- Sun Yat-sen University (CN)
Publication Details
- Journal
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2025-02-04
- DOI
- https://doi.org/10.1109/tits.2025.3534717
- Citations
- 2
- Primary Topic
- Traffic control and management
- Type
- article
- Field-Weighted Citation Impact
- 2.43
Funders
- National Natural Science Foundation of China
- Key Research and Development Projects of Shaanxi Province