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

Institutions

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

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Optimal Sequential Merging Strategy Based on Adaptive Threshold for Ramp Traffic Involving Platoons

Yun Meng, Panpan Yang, Ping Wang, Yinli Jin et al.
2 citations
IEEE Transactions on Intelligent Transportation Systems
Traffic control and management
2.43
article

Optimal Sequential Merging Strategy Based on Adaptive Threshold for Ramp Traffic Involving Platoons

Yun Meng, Panpan Yang, Ping Wang, Yinli Jin, Shengfang Liao
article en
2 citations

Abstract

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.

IEEE Transactions on Intelligent Transportation SystemsVol. 26(4)
Sun Yat-sen University (CN)
National Natural Science Foundation of China, Key Research and Development Projects of Shaanxi Province
Climate action
Openalex Percentile: Top 14%
Traffic control and management
2.43
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.