Understanding the merging behavior patterns and evolutionary mechanism at freeway on-Ramps

Zhang, Yue; Zou, Yajie; Han, Lingtao Wuand Wanbing

doi:10.1080/15472450.2022.2069501

Cited by 6 publications

(4 citation statements)

References 34 publications

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“…To obtain these discrete interaction patterns, the heterogeneous components in the lane‐changing scenarios need to be decomposed first. Gaussian HMM is a commonly used method to explore the hidden state in dynamic driving behavior (Yue Zhang, Zou, Wang, et al., 2022; Yue Zhang et al., 2023) and is adopted in this study to analyze the dynamics of lane‐changing vehicle interactions as illustrated in Figure 2.…”

Section: Methodsmentioning

confidence: 99%

Identifying dynamic interaction patterns in mandatory and discretionary lane changes using graph structure

Zhang,

Zou,

Xie

et al. 2023

Computer aided Civil Eng

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A quantitative understanding of dynamic lane‐changing interaction patterns is indispensable for improving the decision‐making of autonomous vehicles (AVs), especially in mixed traffic with human‐driven vehicles. This paper develops a novel framework combining the hidden Markov model (HMM) and graph structure to identify the difference in dynamic interaction patterns between mandatory lane changes (MLC) and discretionary lane changes (DLC). An HMM is developed to separate the interaction patterns considering heterogeneity in lane‐changing processes and reveal the temporal properties of these patterns. Conditional mutual information is used to quantify the interaction intensity, and the graph structure is used to characterize the relationship between vehicles. Finally, a case study is conducted to demonstrate the practical value of the proposed framework and validate its effectiveness in predicting lane‐changing trajectories. Based on the lane‐changing events extracted from a real‐world trajectory dataset, the proposed analytical framework is applied to model MLC and DLC under congested traffic with levels of service E and F. The results show that there could be multiple heterogeneous dynamic interaction patterns in a lane‐changing process. A comparison of MLC and DLC demonstrates that MLC involves more intense interactions and more frequent transitions of the interaction network structure, while the evolution rules of interaction patterns in DLC do not exhibit a clear trend. The findings in this study are useful for understanding the connectivity structure between vehicles in lane‐changing interactions and for designing safe and smooth driving decision‐making models for AVs and advanced driver‐assistance systems.

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Section: Methodsmentioning

confidence: 99%

Identifying dynamic interaction patterns in mandatory and discretionary lane changes using graph structure

Zhang,

Zou,

Xie

et al. 2023

Computer aided Civil Eng

Self Cite

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“…(2) Classification of Merging Patterns Existing studies categorized merging types into free merging, cooperative merging, and forced merging, with a focus on the impact of merging vehicles on lead and lag vehicles in the target lane [7]. Zhang et al categorized merging patterns into nine modes based on the merging speed and acceleration [6]. Jiang categorized merging patterns into four types-no cars, cars in front, cars behind, and cars both in front and behind [8].…”

Section: Of 19 (1) Data Processing and Trajectory Extractionmentioning

confidence: 99%

“…Instead, they identified a new phenomenon of mutual progressive consistency between merging vehicles and the following vehicles in the target lane, defining it as the "Forced-Cooperative Merging". Zhang et al considered velocity and acceleration variables and introduced the time-series K-means clustering method to partition segments of merging trajectories into nine merging behavior patterns [6].…”

Section: Merging Pattern Analysismentioning

confidence: 99%

“…Among existing research, gap acceptance theory has been extensively explored, which provided a simplified framework for understanding merging behavior mechanism based on gap size and surrounding vehicle positions [3][4][5]. Some scholars have also delved into the impact of external variables on merging behavior at a medium granularity [6], and have thereby further strengthened the understanding of merging behavior mechanism.…”

Section: Introductionmentioning

confidence: 99%

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Unraveling Spatial–Temporal Patterns and Heterogeneity of On-Ramp Vehicle Merging Behavior: Evidence from the exiD Dataset

Wang,

Li,

et al. 2024

Applied Sciences

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Understanding the spatiotemporal characteristics of merging behavior is crucial for the advancement of autonomous driving technology. This study aims to analyze on-ramp vehicle merging patterns, and investigate how various factors, such as merging scenarios and vehicle types, influence driving behavior. Initially, a framework based on a high-definition (HD) map is developed to extract trajectory information in a meticulous manner. Subsequently, eight distinct merging patterns are identified, with a thorough examination of their behavioral characteristics from both temporal and spatial perspectives. Merging behaviors are examined temporally, encompassing the sequence of events from approaching the on-ramp to completing the merge. This study specifically analyzes the target lane’s spatial characteristics, evaluates the merging distance (ratio), investigates merging speed distributions, compares merging patterns and identifies high-risk situations. Utilizing the latest aerial dataset, exiD, which provides HD map data, the study presents novel findings. Specifically, it uncovers patterns where the following vehicle in the target lane chooses to accelerate and overtake rather than cutting in front of the merging vehicle, resulting in Time-to-Collision (TTC) values of less than 2.5 s, indicating a significantly higher risk. Moreover, the study finds that differences in merging speed, distance, and duration can be disregarded in patterns where vehicles are present both ahead and behind, or solely ahead, suggesting these patterns could be integrated for simulation to streamline analysis and model development. Additionally, the practice of truck platooning has a significant impact on vehicle merging behavior. Overall, this study enhances the understanding of merging behavior, facilitating autonomous vehicles’ ability to comprehend and adapt to merging scenarios. Furthermore, this research is significant in improving driving safety, optimizing traffic management, and enabling the effective integration of autonomous driving systems with human drivers.

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Lane Change Behavior Patterns and Risk Analysis in Expressway Weaving Areas: Unsupervised Data-Mining Method

Guo,

Gu,

Chen

et al. 2024

J. Transp. Eng., Part A: Systems

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Understanding the merging behavior patterns and evolutionary mechanism at freeway on-Ramps

Cited by 6 publications

References 34 publications

Identifying dynamic interaction patterns in mandatory and discretionary lane changes using graph structure

Identifying dynamic interaction patterns in mandatory and discretionary lane changes using graph structure

Unraveling Spatial–Temporal Patterns and Heterogeneity of On-Ramp Vehicle Merging Behavior: Evidence from the exiD Dataset

Lane Change Behavior Patterns and Risk Analysis in Expressway Weaving Areas: Unsupervised Data-Mining Method

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