VR Simulator–Based Study to Explore Pedestrian–Vehicle Collisions at Unsignalized Intersections.
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| Title: | VR Simulator–Based Study to Explore Pedestrian–Vehicle Collisions at Unsignalized Intersections. |
|---|---|
| Authors: | Wang, Bo1 (AUTHOR), Zhang, Jian1,2 (AUTHOR) jianzhang@seu.edu.cn, Qian, Yu1 (AUTHOR), Shi, Xiaomeng1 (AUTHOR), Ye, Mao3 (AUTHOR), Ghosh, Indrajit (AUTHOR) indrajit.ghosh@ce.iitr.ac.in |
| Source: | Journal of Advanced Transportation. 6/26/2026, Vol. 2026, p1-17. 17p. |
| Subjects: | Virtual reality, Road interchanges & intersections, Pedestrian accidents, Logistic regression analysis, Disease risk factors, Autonomous vehicles, Psychosocial factors |
| Geographic Terms: | China |
| Abstract: | The unsignalized intersections are areas where right‐of‐way rules are complex, and pedestrians face an increased risk of injury. Despite extensive research on pedestrian behaviors at intersections, there is a need for comprehensive studies that systematically identify the factors influencing pedestrian–vehicle collisions at unsignalized intersections to enhance pedestrian safety in a targeted manner. This research, therefore, investigated the pedestrian crossing behavior in a simulator‐based virtual reality environment, with a total of twenty‐seven variables (e.g., gender, age, area of residence, and history of jaywalking) and 784 samples. Then, a binary logistic regression model was employed to examine the specific impacts of these factors on pedestrian–vehicle collisions from the perspective of pedestrian behaviors, individual characteristics, and external environments. Results showed that males and younger individuals are more likely to have a higher risk of pedestrian–vehicle collisions than females and middle‐aged individuals; those who have a history of jaywalking were more likely to have a higher risk of pedestrian–vehicle collisions; the Myers–Briggs Type Indicator (MBTI) of ENFP, ENTP, INFP, ESFP, and INTP significantly increase the risk of pedestrian–vehicle collisions; and the presence of oversize vehicles in critical areas and implementation interventions reduce the risk of pedestrian–vehicle collisions. Conversely, factors such as alcohol consumption, carrying luggage, and using mobile phones all contribute to an increased risk of pedestrian–vehicle collisions; more observations before crossing intersections and a greater number of pedestrians waiting in the same direction can both reduce the risk of pedestrian–vehicle collisions. The findings not only provide a theoretical basis for predicting pedestrian–vehicle collisions and enhancing active prevention strategies for pedestrian safety but also contribute to creating a safer driving environment for autonomous vehicles in real‐world settings within the context of China. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Advanced Transportation is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 194918731 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: VR Simulator–Based Study to Explore Pedestrian–Vehicle Collisions at Unsignalized Intersections. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wang%2C+Bo%22">Wang, Bo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Jian%22">Zhang, Jian</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> jianzhang@seu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Qian%2C+Yu%22">Qian, Yu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shi%2C+Xiaomeng%22">Shi, Xiaomeng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ye%2C+Mao%22">Ye, Mao</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ghosh%2C+Indrajit%22">Ghosh, Indrajit</searchLink> (AUTHOR)<i> indrajit.ghosh@ce.iitr.ac.in</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Advanced+Transportation%22">Journal of Advanced Transportation</searchLink>. 6/26/2026, Vol. 2026, p1-17. 17p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Virtual+reality%22">Virtual reality</searchLink><br /><searchLink fieldCode="DE" term="%22Road+interchanges+%26+intersections%22">Road interchanges & intersections</searchLink><br /><searchLink fieldCode="DE" term="%22Pedestrian+accidents%22">Pedestrian accidents</searchLink><br /><searchLink fieldCode="DE" term="%22Logistic+regression+analysis%22">Logistic regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Disease+risk+factors%22">Disease risk factors</searchLink><br /><searchLink fieldCode="DE" term="%22Autonomous+vehicles%22">Autonomous vehicles</searchLink><br /><searchLink fieldCode="DE" term="%22Psychosocial+factors%22">Psychosocial factors</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The unsignalized intersections are areas where right‐of‐way rules are complex, and pedestrians face an increased risk of injury. Despite extensive research on pedestrian behaviors at intersections, there is a need for comprehensive studies that systematically identify the factors influencing pedestrian–vehicle collisions at unsignalized intersections to enhance pedestrian safety in a targeted manner. This research, therefore, investigated the pedestrian crossing behavior in a simulator‐based virtual reality environment, with a total of twenty‐seven variables (e.g., gender, age, area of residence, and history of jaywalking) and 784 samples. Then, a binary logistic regression model was employed to examine the specific impacts of these factors on pedestrian–vehicle collisions from the perspective of pedestrian behaviors, individual characteristics, and external environments. Results showed that males and younger individuals are more likely to have a higher risk of pedestrian–vehicle collisions than females and middle‐aged individuals; those who have a history of jaywalking were more likely to have a higher risk of pedestrian–vehicle collisions; the Myers–Briggs Type Indicator (MBTI) of ENFP, ENTP, INFP, ESFP, and INTP significantly increase the risk of pedestrian–vehicle collisions; and the presence of oversize vehicles in critical areas and implementation interventions reduce the risk of pedestrian–vehicle collisions. Conversely, factors such as alcohol consumption, carrying luggage, and using mobile phones all contribute to an increased risk of pedestrian–vehicle collisions; more observations before crossing intersections and a greater number of pedestrians waiting in the same direction can both reduce the risk of pedestrian–vehicle collisions. The findings not only provide a theoretical basis for predicting pedestrian–vehicle collisions and enhancing active prevention strategies for pedestrian safety but also contribute to creating a safer driving environment for autonomous vehicles in real‐world settings within the context of China. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Advanced Transportation is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=194918731 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1155/atr/3841145 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 1 Subjects: – SubjectFull: Virtual reality Type: general – SubjectFull: Road interchanges & intersections Type: general – SubjectFull: Pedestrian accidents Type: general – SubjectFull: Logistic regression analysis Type: general – SubjectFull: Disease risk factors Type: general – SubjectFull: Autonomous vehicles Type: general – SubjectFull: Psychosocial factors Type: general – SubjectFull: China Type: general Titles: – TitleFull: VR Simulator–Based Study to Explore Pedestrian–Vehicle Collisions at Unsignalized Intersections. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wang, Bo – PersonEntity: Name: NameFull: Zhang, Jian – PersonEntity: Name: NameFull: Qian, Yu – PersonEntity: Name: NameFull: Shi, Xiaomeng – PersonEntity: Name: NameFull: Ye, Mao – PersonEntity: Name: NameFull: Ghosh, Indrajit IsPartOfRelationships: – BibEntity: Dates: – D: 26 M: 06 Text: 6/26/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 01976729 Numbering: – Type: volume Value: 2026 Titles: – TitleFull: Journal of Advanced Transportation Type: main |
| ResultId | 1 |
