Bicyclist Behavior at Stop Signs

Ayres, Thomas J.; Kelkar, Rajeev; Kubose, Tate; Shekhawat, Vivek K.

doi:10.1177/1541931215591350

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…To further study their perception and cognitive states, human sensing devices (e.g. physiology devices) have shown to provide promising insights (Ridel et al 2018;Trefzger et al 2018;Ayres et al 2015). There are practical concerns about the data collection of human sensing on real roads.…”

Section: Integration Of Human Sensing Within Ivementioning

confidence: 99%

ORCLSim: A System Architecture for Studying Bicyclist and Pedestrian Physiological Behavior Through Immersive Virtual Environments

Guo,

Angulo,

Robartes

et al. 2021

Preprint

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Injuries and fatalities for vulnerable road users, especially bicyclists and pedestrians, are on the rise. To better inform design for vulnerable road users, we need to conduct more studies to evaluate how bicyclist and pedestrian behavior and physiological states change in different roadway designs and contextual settings. Previous research highlights the advantages of Immersive Virtual Environment (IVE) in conducting bicyclist and pedestrian studies. These environments do not put participants at risk of getting injured, are low-cost compared to on-road or naturalistic studies and allow researchers to fully control variables of interest. In this paper, we propose a framework ORCLSim, to support human sensing techniques within IVE to evaluate bicyclist and pedestrian physiological and behavioral changes in different contextual settings. To showcase this framework, we present two case studies where we collect and analyze pilot data from five participants' physiological and behavioral responses in an IVE setting, representing real-world roadway segments and traffic conditions. Results from these case studies indicate that physiological data is sensitive to road environment changes and real-time events, especially changes in heart rate and gaze behavior. Additionally, our preliminary data indicates participants may respond differently to various roadway settings (e.g., intersections with or without traffic signal). By analyzing these changes, we can identify how participants' stress levels and cognitive load is impacted by the simulated surrounding environment. The ORCLSim system architecture can be further utilized for future studies in users' behavioral and physiological responses in different virtual reality settings.

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Section: Integration Of Human Sensing Within Ivementioning

confidence: 99%

ORCLSim: A System Architecture for Studying Bicyclist and Pedestrian Physiological Behavior Through Immersive Virtual Environments

Guo,

Angulo,

Robartes

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The studies investigated how cyclists behave at stop signs. It was consistently found that cyclists rarely or never stop at stop signs (Ayres et al, 2015;Silva, 2015) In addition, various studies have shown how cyclists behave at the stop line when it is in front of the stop line of motorised traffic (Loskorn, 2010;Mangundu & Koorey, 2010). Bicycle boxes in front of or next to the stop line of motorised traffic can increase cyclists' sense of safety and cyclists are more visible to motorists.…”

Section: Introductionmentioning

confidence: 99%

Analysis of stopping behaviour of cyclists at a traffic light-controlled intersection using trajectory data

Leschik,

Zhang,

Gimm

2023

The Evolving Scholar - BMD 2023, 5th Edition

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Cyclists have various route options to get to their destination. They can share lanes with vehicles, share lanes with pedestrians, or have their own lane. In Germany there are often marked lanes across intersections and stop lines in front of the crossing, guiding the cyclists their way. However, these markings are not always respected in the way they should be. This study is intended to examine the stopping behaviour of cyclists at a traffic light-controlled intersection. A distinction was made between single cyclists (n = 1,411) and cyclist groups (more than one cyclist; n = 475). The stopping area was divided into polygons to understand where most people stop before an intersection. Furthermore, it was examined where people continued to ride after stopping (bicycle or pedestrian crossing) and this was compared with cyclists who did not stop. The aim of this study is to investigate cyclists’ stopping behaviour (e.g. stopping position) at intersections with consideration of the impact of groups, wrong-way cycling and road usage. It is to be investigated whether single cyclists behave differently than cyclist groups and whether there are differences in the two groups for wrong way cyclists. Both - single cyclists (69.38%) and cyclist groups (84.57%) - crossed the intersection more frequently without stopping within the observation period. In all cases, cyclists stopped mostly at the bicycle stopping line or used the bicycle crossing, thereby complying with the law. Most wrong way cyclists on the bicycle crossing were found for single cyclists with stopping (10%, n = 27) and cyclist groups with stopping (8%, n = 12). The speeds between single cyclists and cyclist groups differ slightly, and the stopping behaviour is very similar if the bicycle crossing is used after the stop. The study shows that cyclists also stop far away from the intersection at unexpected positions that may not be in the field of vision of autonomous vehicles. Nevertheless, the cyclists still cross the intersection, partly also on the footpath, which is not rule compliant and unexpected. The information can be used to improve models of cyclists’ behaviour, for example, in microscopic simulations, in which cyclists only stop at clearly defined locations. Knowledge about the stopping position and the further crossing of the intersection can help to make simulations more realistic, e.g. when planning or changing new infrastructure. Furthermore, the results of this study will provide further knowledge, which helps developing autonomous driving functions to correctly anticipate cycling behaviour at intersections.

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Difference between car-to-cyclist crash and near crash in a perpendicular crash configuration based on driving recorder analysis

Ito

Hayakawa

Kondo

et al. 2018

Accident Analysis & Prevention

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Bicyclist Behavior at Stop Signs

Cited by 5 publications

References 13 publications

ORCLSim: A System Architecture for Studying Bicyclist and Pedestrian Physiological Behavior Through Immersive Virtual Environments

ORCLSim: A System Architecture for Studying Bicyclist and Pedestrian Physiological Behavior Through Immersive Virtual Environments

Analysis of stopping behaviour of cyclists at a traffic light-controlled intersection using trajectory data

Difference between car-to-cyclist crash and near crash in a perpendicular crash configuration based on driving recorder analysis

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