مهندسی ترافیک

مهندسی ترافیک

طراحی تقاطع‌های ایمن: بررسی تأثیر هشدارهای دیداری و شنیداری بر تخلف عبور عابر ‌پیاده از چراغ‌قرمز با استفاده از محیط واقعیت مجازی (VR)

نوع مقاله : مقاله پژوهشی

نویسندگان
سجاد مظلوم 1
نوید خادمی 2
امیرمحمد ذبیح پور 3
1 کارشناسی ارشد، دستیار پژوهشی دانشکده مهندسی عمران دانشگاه تهران، تهران، ایران
2 دانشیار، دانشکده مهندسی عمران دانشگاه تهران، تهران، ایران
3 دانشجوی کارشناسی ارشد دانشکده مهندسی عمران دانشگاه تهران، تهران، ایران
چکیده
ایمنی عابران به‌عنوان اعضای آسیب‌پذیر راه‌ها یکی از جدی‌ترین نگرانی‌های دنیای حمل‌ونقل محسوب می‌شود. فناوری واقعیت مجازی (VR) به‌عنوان ابزاری ارزشمند برای بررسی ادراک و رفتار انسان در محیط‌های کنترل‌شده، فراگیر و بدون ریسک در حال ظهور است. این مطالعه از فناوری VR برای بررسی اثربخشی هشدارهای پیشگیرانه دیداری و شنیداری برای عابران پیاده در تقاطع‌های چراغ‌دار، تحت شرایط محیطی و ترافیکی متفاوت استفاده کرد. به‌طور خاص، این مطالعه چهار نوع سیگنال عابر پیاده شامل چراغ عابر معمولی، دیوار لیزری، خط عابر پیاده بصری و سیگنال صوتی را آزمایش کرد. برای تحلیل روابط بین نوع سیگنال عابر پیاده و رفتار تخلف، یک مدل معادلات ساختاری (SEM) با دو متغیر میانجی پنهان - آگاهی محیطی و درک خطر - توسعه داده شد. درمجموع 245 شرکت‌کننده در آزمایش‌ها شرکت کردند و از تقاطع‌های شبیه‌سازی‌شده VR که به سیگنال‌های پیشنهادی مجهز شده بودند عبور کردند و سناریوهای مختلف محیطی و ترافیکی را تجربه کردند. داده‌های رفتاری (تخلف افراد) به‌طور مستقیم از شبیه‌ساز و داده‌های پرسشنامه‌ای از افراد برداشت شد. ‌یافته‌ها نشان داد که تجهیز تقاطع‌ها به هشدارهای کمکی می‌تواند درک خطر و آگاهی محیطی عابران پیاده را بهبود بخشد و درنتیجه تخلف عابران پیاده را کاهش دهد. علاوه بر این، استفاده از سیگنال‌های بصری به‌ویژه در شب و در بین مردان در کاهش تخلفات نویدبخش بودند. همچنین این مطالعه ارتباطی منفی بین درک خطر و آگاهی محیطی افراد با تخلف عابران یافت. نتایج این پژوهش می‌تواند به‌عنوان راهنمایی برای طراحی و استفاده از چراغ‌های عابر هوشمند به‌منظور ارتقای ایمنی عابران مورداستفاده قرار گیرد.
کلیدواژه‌ها
ایمنی عابران؛ واقعیت مجازی (VR)
رفتار عبور از خیابان
هشدارهای بصری و صوتی
ادراک
فهم و رفتار
  • شهرداری تهران, "سالنامه آماری تصادفات و تلفات ترافیکی شهر تهران," 1400.

 

 

  • NHTSA, "Traffic Safety Facts 2012 Data," U.S. Department of Transportation, National Highway Transportation Safety Administration, 2014.

 

 

 

  • C. de Lavalette, C. Tijus, S. Poitrenaud, C. Leproux, J. Bergeron, and J.-P. Thouez, "Pedestrian crossing decision-making: A situational and behavioral approach," Safety science, vol. 47, no. 9, pp. 1248-1253, 2009.

 

  • J. King, D. Soole, and A. Ghafourian, "Illegal pedestrian crossing at signalised intersections: incidence and relative risk," Accident Analysis & Prevention, vol. 41, no. 3, pp. 485-490, 2009.

 

  • Gu, D. E. Angelaki, and G. C. DeAngelis, "Neural correlates of multisensory cue integration in macaque MSTd," Nature neuroscience, vol. 11, no. 10, pp. 1201-1210, 2008.

 

  • Brosseau, S. Zangenehpour, N. Saunier, and L. Miranda-Moreno, "The impact of waiting time and other factors on dangerous pedestrian crossings and violations at signalized intersections: A case study in Montreal," Transportation research part F: traffic psychology and behaviour, vol. 21, pp. 159-172, 2013.

 

  • Diependaele, "Non-compliance with pedestrian traffic lights in Belgian cities," Transportation Research Part F: Traffic Psychology and Behaviour, vol. 67, pp. 230-241, 2019.

 

  • A. Arhin and E. C. Noel, "Impact of countdown pedestrian signals on pedestrian behavior and perception of intersection safety in the District of Columbia," in 2007 IEEE Intelligent Transportation Systems Conference, 2007: IEEE, pp. 337-342.

 

  • Lipovac, M. Vujanic, B. Maric, and M. Nesic, "The influence of a pedestrian countdown display on pedestrian behavior at signalized pedestrian crossings," Transportation research part F: traffic psychology and behaviour, vol. 20, pp. 121-134, 2013.

 

  • Paschalidis, I. Politis, S. Basbas, and P. Lambrianidou, "Pedestrian compliance and cross walking speed adaptation due to countdown timer installations: A self report study," Transportation research part F: traffic psychology and behaviour, vol. 42, pp. 456-467, 2016.

 

  • Khatoon, G. Tiwari, and N. Chatterjee, "Impact of grade separator on pedestrian risk taking behavior," Accident Analysis & Prevention, vol. 50, pp. 861-870, 2013.

 

  • D. Egan, "Children’s gaze behaviour at real-world and simulated road crossings," 2012.

 

  • C. Schwebel and L. A. McClure, "Using virtual reality to train children in safe street-crossing skills," Injury prevention, vol. 16, no. 1, pp. e1-e1, 2010.

 

  • Mukherjee and S. Mitra, "A comparative study of safe and unsafe signalized intersections from the view point of pedestrian behavior and perception," Accident Analysis & Prevention, vol. 132, p. 105218, 2019.

 

  • B. Sitkin and A. L. Pablo, "Reconceptualizing the determinants of risk behavior," Academy of management review, vol. 17, no. 1, pp. 9-38, 1992.

 

  • Sjöberg, B.-E. Moen, and T. Rundmo, "Explaining risk perception," An evaluation of the psychometric paradigm in risk perception research, vol. 10, no. 2, pp. 665-612, 2004.

 

  • -H. Kwon, J. Kim, S. Kim, and G.-H. Cho, "Pedestrians safety perception and crossing behaviors in narrow urban streets: An experimental study using immersive virtual reality technology," Accident Analysis & Prevention, vol. 174, p. 106757, 2022.

 

  • Rankavat and G. Tiwari, "Pedestrians risk perception of traffic crash and built environment features–Delhi, India," Safety science, vol. 87, pp. 1-7, 2016.

 

  • Villaveces et al., "Pedestrians' perceptions of walkability and safety in relation to the built environment in Cali, Colombia, 2009–10," Injury prevention, vol. 18, no. 5, pp. 291-297, 2012.

 

  • Zhang, F. Chen, and Y. Wei, "Evaluation of pedestrian crossing behavior and safety at uncontrolled mid-block crosswalks with different numbers of lanes in China," Accident Analysis & Prevention, vol. 123, pp. 263-273, 2019.

 

  • C. Diógenes, L. A. Lindau, and L. d. S. de Transportes–LASTRAN, "Risk perception of pedestrians at midblock crossings in Brazil," in Proceedings of the 12th World Conference on Transport Research (WCTR), 2010, pp. 1-19.

 

  • Lee, Y. Park, J. Kim, and G.-H. Cho, "Association between intersection characteristics and perceived crash risk among school-aged children," Accident Analysis & Prevention, vol. 97, pp. 111-121, 2016.

 

  • He et al., "Safety of micro-mobility: Riders’ psychological factors and risky behaviors of cargo TTWs in China," Transportation research part F: traffic psychology and behaviour, vol. 80, pp. 189-202, 2021.

 

  • Cœugnet, B. Cahour, and S. Kraiem, "Risk-taking, emotions and socio-cognitive dynamics of pedestrian street-crossing decision-making in the city," Transportation research part F: traffic psychology and behaviour, vol. 65, pp. 141-157, 2019.

 

  • Kim, H. Kim, Y. Kwon, S. Choi, and G. Shin, "Performance of ground-level signal detection when using a phone while walking," Accident Analysis & Prevention, vol. 151, p. 105909, 2021.

 

  • S. Larue and C. N. Watling, "Acceptance of visual and audio interventions for distracted pedestrians," Transportation research part F: traffic psychology and behaviour, vol. 76, pp. 369-383, 2021.

 

  • M. Taylor, "Situational awareness rating technique (SART): The development of a tool for aircrew systems design," in Situational awareness: Routledge, 2017, pp. 111-128.

 

  • Jamali-Dolatabad, H. Sadeghi-Bazargani, and P. Sarbakhsh, "Predictors of fatal outcomes in pedestrian accidents in Tabriz Metropolis of Iran: application of PLS-DA method," Traffic injury prevention, vol. 20, no. 8, pp. 873-879, 2019.

 

  • M. Poó, R. D. Ledesma, and R. Trujillo, "Pedestrian crossing behavior, an observational study in the city of Ushuaia, Argentina," Traffic injury prevention, vol. 19, no. 3, pp. 305-310, 2018.

 

  • Rosenbloom, "Crossing at a red light: Behaviour of individuals and groups," Transportation research part F: traffic psychology and behaviour, vol. 12, no. 5, pp. 389-394, 2009.

 

  • Dommes, M.-A. Granié, M.-S. Cloutier, C. Coquelet, and F. Huguenin-Richard, "Red light violations by adult pedestrians and other safety-related behaviors at signalized crosswalks," Accident Analysis & Prevention, vol. 80, pp. 67-75, 2015.

 

  • Ren, Z. Zhou, W. Wang, Y. Zhang, and W. Wang, "Crossing behaviors of pedestrians at signalized intersections: observational study and survey in China," Transportation research record, vol. 2264, no. 1, pp. 65-73, 2011.

 

  • M. Hamed, "Analysis of pedestrians’ behavior at pedestrian crossings," Safety science, vol. 38, no. 1, pp. 63-82, 2001.

 

  • Yagil, "Beliefs, motives and situational factors related to pedestrians’ self-reported behavior at signal-controlled crossings," Transportation Research Part F: Traffic Psychology and Behaviour, vol. 3, no. 1, pp. 1-13, 2000.

 

  • Yoneda, N. Suganuma, R. Yanase, and M. Aldibaja, "Automated driving recognition technologies for adverse weather conditions," IATSS research, vol. 43, no. 4, pp. 253-262, 2019.

 

  • N. Schmitz, "The effects of pedestrian countdown timers on safety and efficiency of operations at signalized intersections," 2011.

 

  • Wang, H. Huang, P. Xu, S. Xie, and S. Wong, "Random parameter probit models to analyze pedestrian red-light violations and injury severity in pedestrian–motor vehicle crashes at signalized crossings," Journal of Transportation Safety & Security, vol. 12, no. 6, pp. 818-837, 2020.

 

  • G. Kang and B. S. Kim, "A Study on Cognitive Error Validation for LED In-Ground Traffic Lights Using a Digital Twin and Virtual Environment," Mathematics, vol. 11, no. 17, p. 3780, 2023.
  • F. Hair, G. T. M. Hult, C. M. Ringle, and M. Sarstedt, A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications, 2021.

 

  • Fornell and D. F. Larcker, "Evaluating structural equation models with unobservable variables and measurement error," Journal of marketing research, vol. 18, no. 1, pp. 39-50, 1981.

 

  • Zhou, S. B. Romero, and X. Qin, "An extension of the theory of planned behavior to predict pedestrians’ violating crossing behavior using structural equation modeling," Accident Analysis & Prevention, vol. 95, pp. 417-424, 2016.