Modelling risk perception using a dynamic hybrid choice model and brain-imaging data: An application to virtual reality cycling

Bogacz, Martyna; Hess, Stephane; Calastri, Chiara; Choudhury, Charisma F.; Mushtaq, Faisal; Awais, Muhammad; Nazemi, Mohsen; Eggermond, Michael A.B. van; Erath, Alexander

doi:10.1016/j.trc.2021.103435

Cited by 16 publications

(22 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, it is important to note the limitation in statistical significance associated with the tested sample size. A sample size of 50 participants was settled on considering time and budget limitations of the project, as well as comparable sample sizes used in previous similar studies, although larger (and more-diverse and representative) sample sizes would improve the generalizability of the findings (73)(74)(75).…”

Section: Discussionmentioning

confidence: 99%

“…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). Among this small body of existing literature, Nazemi et al used a bike simulator and immersive VR to test perceived level of safety and willingness to bicycle, along with pre-test and post-test questionnaires (72).…”

Section: Cycling Tests In Vr Simulatorsmentioning

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…At the same time, many tasks may be automated to some degree by means of urban sensors and big data analysis. In line with other recent advances which take profit of new and extensive data-sets 6,18,29,41 , machine learning 4,5,5,10,43 and their combination with complex networks tools 16,19,28,30,49,61 , our work focuses on the automated assessment and improvement of urban traffic safety, for both pedestrian and vehicles.…”

Section: Discussion Limitations and Conclusionmentioning

confidence: 99%

“…Up to now, due to lack of available (or open) data and tools for automated analysis, these studies have mostly been constrained either to the limited features available from planimetric map data 16,17,23 , or to detailed, but time-consuming, descriptions of particular settings collected by hand 20,21 . However, the rise of new Big Data sources 13,[24][25][26] related to urban environments and transportation has boosted the development of new techniques 10,[26][27][28] and the combination of complementary tools existing in different fields 5,29 , such as complex systems 18,30 .…”

Section: Introductionmentioning

confidence: 99%

Predicting the impact of urban change in pedestrian and road safety

Bustos¹,

Rhoads²,

Lapedriza³

et al. 2022

Preprint

View full text Add to dashboard Cite

Increased interaction between and among pedestrians and vehicles in the crowded urban environments of today gives rise to a negative side-effect: a growth in traffic accidents, with pedestrians being the most vulnerable elements. Recent work has shown that Convolutional Neural Networks are able to accurately predict accident rates exploiting Street View imagery along urban roads. The promising results point to the plausibility of aided design of safe urban landscapes, for both pedestrians and vehicles. In this paper, by considering historical accident data and Street View images, we detail how to automatically predict the impact (increase or decrease) of urban interventions on accident incidence. The results are positive, rendering an accuracies ranging from 60 to 80%. We additionally provide an interpretability analysis to unveil which specific categories of urban features impact accident rates positively or negatively. Considering the transportation network substrates (sidewalk and road networks) and their demand, we integrate these results to a complex network framework, to estimate the effective impact of urban change on the safety of pedestrians and vehicles. Results show that public authorities may leverage on machine learning tools to prioritize targeted interventions, since our analysis show that limited improvement is obtained with current tools. Further, our findings have a wider application range such as the design of safe urban routes for pedestrians or to the field of driver-assistance technologies.

show abstract

“…For example, bicyclists' galvanic skin response is found to have less peaks with a bike lane than in no bike lane condition [6]. In another cycling virtual reality study, EEG data show its potential in a hybrid model framework as an indicator of the perceived risk of bicyclists [29]. For pedestrians, it is notable that older pedestrians spent more time focusing on their travel path and rarely on other areas in the last five seconds before making the crossing decision in an IVE study [30].…”

Section: Introductionmentioning

confidence: 94%

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

Guo

Angulo

Robartes

et al. 2022

Journal of Advanced Transportation

View full text Add to dashboard Cite

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 evaluate how bicyclist and pedestrian behavior and physiological states change in different roadway design and contextual settings. Previous research highlights the advantages of using immersive virtual environments (IVEs) in conducting bicyclist and pedestrian studies. These environments do not put participants at risk of injury, 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, Omni-Reality and Cognition Lab Simulator (ORCLSim), to support human sensing techniques within IVEs to evaluate bicyclist and pedestrian physiological and behavioral changes in different contextual settings. To showcase this framework, we present two case studies, where pilot data from five participants’ physiological and behavioral responses in an IVE setting are collected and analyzed, representing real-world roadway segments and traffic conditions. Results from these case studies indicate that physiological data are sensitive to road environment changes and real-time events in the IVE, especially changes in heart rate and gaze behavior. In addition, our preliminary data indicate participants may respond differently to various roadway settings (e.g., signalized vs. unsignalized intersections). By analyzing these changes, future studies can identify how participants’ stress level and cognitive load are impacted by the surrounding environment. The ORCLSim system architecture is a prototype that can be customized for future studies in understanding users’ behavioral and physiological responses in virtual reality settings.

show abstract

Modelling risk perception using a dynamic hybrid choice model and brain-imaging data: An application to virtual reality cycling

Cited by 16 publications

References 64 publications

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

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

Predicting the impact of urban change in pedestrian and road safety

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

Contact Info

Product

Resources

About