How gaps are created during anticipation of lane changes

Chen, Kequan; Knoop, Victor L.; Liu, Pan; Li, Zhibin; Wang, Yu-Xuan

doi:10.1080/21680566.2022.2152129

Cited by 6 publications

(6 citation statements)

References 47 publications

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“…In actual traffic, drivers typically exhibit cooperative behavior (e.g. Kondyli and Elefteriadou, 2012), including the phenomenon of 'anticipation' by the new follower (Chen et al, 2023) and 'relaxation' after the lane change has occurred Zheng et al (2013). The latter entails that drivers sometimes tolerate small gaps, knowing that these gaps will soon expand again (Daamen et al, 2010).…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

Now or never: Eye tracking and response times reveal the dynamics of highway merging decisions

Zgonnikov,

van Niekerk,

Eisma

et al. 2023

Preprint

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Merging onto a highway is a safety-critical task resulting in a large number of traffic accidents; fundamental research into merging behavior of human drivers can help reduce this toll. Two cognitive processes critical to merging, attention allocation and decision making, have been extensively studied in both natural human behavior in real traffic and simulated driving. However, the interaction between attention and decision making during merging remains poorly understood. To address this gap, here we investigated the relationship between kinematics of the merging scenario, gap acceptance decisions, response timing, and eye movements of drivers during merging. In a video-based experiment, our participants (N=24) repeatedly made gap acceptance decisions based on the information available in the front view and the side mirror while their response times and eye movements were recorded. We found that decisions to accept a gap were much faster than decisions to reject a gap: the dynamically decreasing gap demanded that participants accept gaps in a timely manner. Furthermore, merging decisions and their timing depended on the distance and time-to-arrival to the overtaking vehicle, but also on the time pressure imposed by the approaching end of the merging lane. Most importantly, the proportion of time participants spent looking at the rear-view mirror was associated with differences in the probability of accepting the gap, controlling for duration of the decision-making process. This effect was particularly pronounced in conditions with low time budgets, suggesting that appropriate glancing behavior can be critical for merging decisions under time pressure. Our results lay foundation for future efforts on cognitive modeling of the interplay between decision-making and attention processes during merging and can aid in understanding unsafe merging maneuvers related to improper attention distribution.

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Section: Limitations and Future Directionsmentioning

confidence: 99%

Now or never: Eye tracking and response times reveal the dynamics of highway merging decisions

Zgonnikov,

van Niekerk,

Eisma

et al. 2023

Preprint

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show abstract

“…Then [10] investigated the effect of microscopic characteristics and analyzed the decision-making model by changing the lanes of heavy vehicles. [8], [11], [12], [13] conducted several studies on lane changes aiming to investigate the decision-making process of lane change manoeuvres for various drivers based on a two-stage test-drive, investigate the impact of a lane change strategy on expressway traffic operations, and analyze the behavior of new follower vehicles changing lanes in the target lane when facing anticipation (pre-insertion process). The research conducted has its own variables.…”

Section: Literature Reviewmentioning

confidence: 99%

“…[5] focused on the motivation to change lanes, the selection of the target lane, and the decision to change lanes. Whereas [8], [11], [12], [13] focuses on strategies for changing lanes, time, distance, speed, density, and gap width.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of traffic flow due to lane changes by heavy vehicles

Sari,

Nahry,

Mauramdha

2024

IOP Conf. Ser.: Earth Environ. Sci.

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The transportation of goods via heavy vehicles (HV) plays a crucial role in urban economics. However, the operational and physical characteristics of these vehicles negatively impact traffic flow, leading to reduced speeds and increased congestion. This study examines the influence of HV lane changes on average traffic speed by implementing diverse lane change intensities and alternative strategies that restrict HV to specific lanes, narrowing their lane-changing space. Five HV classes based on axle count were analyzed using Vissim traffic simulation software on a 600 m JORR toll road segment. Three strategies were assessed across various scenarios, with differing HV composition percentages. Results show that HV lane changes, simulated using Vissim, significantly affect average traffic speed. Comparing high and low-intensity lane changes revealed an approximately 12% speed increase. Additionally, every 5% increment in HV composition led to an average 3% traffic speed reduction. In high-density conditions for every 5% increase in the composition of HV from high-intensity compared to medium and low-intensity lane changes, the average speed of traffic flow increases by 4% and 8% in low-density conditions. This research highlights the importance of enforcing designated lane usage for HV on toll roads to enhance traffic flow performance.

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“…Using real-world vehicle-level trajectory data, Yang et al (2019b) examined 5339 LC trajectory samples and found that approximately 44% of new followers would decelerate when they noticed the LC motivation in the adjacent lane. Chen et al (2022) investigated the impact of anticipation on the new follower and found evidence of its significance. To this end, they developed two binary logit models, demonstrating that including the anticipation-related factors significantly improved the prediction accuracy regarding whether the new follower changes its driving behavior during anticipation.…”

Section: Impact Of Lane-changing Anticipationmentioning

confidence: 99%

“…Although some recent empirical studies have also reported similar negative impacts of anticipation on the new follower's behavior (Yang et al, 2019a;Chen et al 2022), relatively limited attention has been paid to quantifying these impacts. As a result, there is currently a lack of clarity regarding a CF model that can accurately describe the new follower's behavior during anticipation.…”

Section: Introductionmentioning

confidence: 99%