Factors impacting bicyclist lateral position and velocity in proximity to commercial vehicle loading zones: Application of a bicycling simulator

Abadi, Masoud Ghodrat; Hurwitz, David; Sheth, Manali; McCormack, Edward; Goodchild, Anne

doi:10.1016/j.aap.2019.01.024

Cited by 9 publications

(4 citation statements)

References 20 publications

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“…In a bicycle simulator experiment, Abadi et al (2019) investigated interactions at loading zones incorporating different HGV behaviors (i.e., no truck, parked truck, truck pulling out) and infrastructure designs (varying road markings and warning signs). The results showed that truck presence does influence cyclist’s performance (i.e., velocity and lateral position), and this effect varies based on the design treatments employed.…”

Section: Resultsmentioning

confidence: 99%

“…From the study by Abadi et al (2019) , which was the only one containing a merging scenario, we identified two examples of HGV-VRU interactive behaviors and communication cues ( Table 5 ). One of them is linked to implicit communication and reflects communication via movements and position (kinesics and proxemics): the presence of an HGV in a loading zone next to a cyclist lane makes the cyclists slow down and choose a lateral placement in the lane further away from the HGV.…”

Section: Resultsmentioning

confidence: 99%

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Interactions Between Heavy Trucks and Vulnerable Road Users—A Systematic Review to Inform the Interactive Capabilities of Highly Automated Trucks

et al. 2022

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This study investigates interactive behaviors and communication cues of heavy goods vehicles (HGVs) and vulnerable road users (VRUs) such as pedestrians and cyclists as a means of informing the interactive capabilities of highly automated HGVs. Following a general framing of road traffic interaction, we conducted a systematic literature review of empirical HGV-VRU studies found through the databases Scopus, ScienceDirect and TRID. We extracted reports of interactive road user behaviors and communication cues from 19 eligible studies and categorized these into two groups: 1) the associated communication channel/mechanism (e.g., nonverbal behavior), and 2) the type of communication cue (implicit/explicit). We found the following interactive behaviors and communication cues: 1) vehicle-centric (e.g., HGV as a larger vehicle, adapting trajectory, position relative to the VRU, timing of acceleration to pass the VRU, displaying information via human-machine interface), 2) driver-centric (e.g., professional driver, present inside/outside the cabin, eye-gaze behavior), and 3) VRU-centric (e.g., racer cyclist, adapting trajectory, position relative to the HGV, proximity to other VRUs, eye-gaze behavior). These cues are predominantly based on road user trajectories and movements (i.e., kinesics/proxemics nonverbal behavior) forming implicit communication, which indicates that this is the primary mechanism for HGV-VRU interactions. However, there are also reports of more explicit cues such as cyclists waving to say thanks, the use of turning indicators, or new types of external human-machine interfaces (eHMI). Compared to corresponding scenarios with light vehicles, HGV-VRU interaction patterns are to a high extent formed by the HGV’s size, shape and weight. For example, this can cause VRUs to feel less safe, drivers to seek to avoid unnecessary decelerations and accelerations, or lead to strategic behaviors due to larger blind-spots. Based on these findings, it is likely that road user trajectories and kinematic behaviors will form the basis for communication also for highly automated HGV-VRU interaction. However, it might also be beneficial to use additional eHMI to compensate for the loss of more social driver-centric cues or to signal other types of information. While controlled experiments can be used to gather such initial insights, deeper understanding of highly automated HGV-VRU interactions will also require naturalistic studies.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Interactions Between Heavy Trucks and Vulnerable Road Users—A Systematic Review to Inform the Interactive Capabilities of Highly Automated Trucks

et al. 2022

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“…Recently, VR simulators and virtual environments have been used as an effective tool for transportation research, and there have been multiple studies related to the development and validation of bicycle simulators and prototypes (12,14,(60)(61)(62)(63)(64)(65)(66)(67)(68)(69)(70)(71). However, there is little prior research of bicyclists' perceived safety or comfort using VR simulators (72)(73)(74)(75).…”

Section: Cycling Tests In Vr Simulatorsmentioning

confidence: 99%

Examining Socioeconomic and Physiological Factors Affecting Preferences for Cycling Infrastructure Using Virtual Reality Experimentation

Saglio,

Robartes,

Chen

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Understanding cyclist behaviors and preferences is critical to improve safety and increase mode share. However, real-world cycling data is scarce and on-road testing presents safety concerns. This study presents a novel way of studying cyclists’ perceptions of bicycle infrastructure design alternatives using immersive virtual environments modeled after a real-world corridor, and a previously validated bike simulator at the University of Virginia, U.S. Three infrastructure scenarios were tested: sharing lanes with motor vehicles marked with sharrows (with no bike lane), a separated bike lane, and a protected bike lane with flexible delineators. Surveys elicited data on participants’ preferences and perceptions of safety, as well as demographic and socioeconomic data. Smartwatches collected heart rate (HR) data. A multinomial logit model examined the relationship between sociodemographic and physiological variables and preferences for bicycle infrastructure. Results suggest gender, age, and abrupt changes in HR affect cyclists’ preferences for bike infrastructure design. Overall, gender emerges as the most practically significant predictor variable for bicycle infrastructure preference, with men more likely to prefer sharrows and women more likely to prefer protected bike lanes. Exploratory analysis also suggests that bicyclists who self-identified as “strong and fearless” were more likely to choose sharrows as the preferred design, while bicyclists who self-identified as “interested but concerned” more often chose the protected bike lane. These results highlight the importance of understanding preferences of not just current cyclists, but potential future cyclists. Virtual reality simulation offers a low-cost, safe, and efficient method to understand preferences of individuals interested but not yet choosing cycling as a mode.

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“…Table 1 summarizes the most important characteristics of the papers dealing with speed perception in a driving simulator studies. Studies related to the perception of the passenger vehicle speed in a driving simulator are covered by the following references [20][21][22][23][24][25][26], while studies related to the perception of the bicycle speed were conducted by many authors [27][28][29]. While driving, evaluation of the vehicle speed and inter-vehicle distance are crucial skills and constant demands.…”

Section: Speed Perception (Driving Simulator)mentioning

confidence: 99%

What Affects the E-Bicycle Speed Perception in the Era of Eco-Sustainable Mobility: A Driving Simulator Study

Simović

Ivanišević²,

Trifunović

et al. 2021

Sustainability

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The increase in the number of electric bicycles worldwide has resulted in a rise in the number of traffic accidents involving e-bicyclists. Previous studies have been based on analyzing the use, advantages and disadvantages of e-bicycles, whereas only a small number of studies have been focused on analyzing the e-bicycle traffic safety, particularly the factors leading to the occurrence of traffic accidents. One of the factors affecting the occurrence of traffic accidents is the incorrect perception of the e-bicycle speed by other traffic participants. To examine the mentioned problem, the authors of this paper conducted an experimental study to determine what affects the e-bicycle speed perception. The experiment included 175 participants, aged 18 to 50. The research was conducted under laboratory conditions using a driving simulator, at different e-bicycle speeds (10 km/h, 20 km/h and 30 km/h), in the situations in which the e-bicyclist was (not) using a reflective vest. The results show statistically significant differences in the e-bicycle speed perception when the e-bicyclist does not use/uses a reflective vest. Besides, the driving licence categories of traffic participants and their driving experience also have a significant impact on the perception of the e-bicycle speed.

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Factors impacting bicyclist lateral position and velocity in proximity to commercial vehicle loading zones: Application of a bicycling simulator

Cited by 9 publications

References 20 publications

Interactions Between Heavy Trucks and Vulnerable Road Users—A Systematic Review to Inform the Interactive Capabilities of Highly Automated Trucks

Interactions Between Heavy Trucks and Vulnerable Road Users—A Systematic Review to Inform the Interactive Capabilities of Highly Automated Trucks

Examining Socioeconomic and Physiological Factors Affecting Preferences for Cycling Infrastructure Using Virtual Reality Experimentation

What Affects the E-Bicycle Speed Perception in the Era of Eco-Sustainable Mobility: A Driving Simulator Study

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