The Journal of The Korea Institute of Intelligent Transport Systems (한국ITS학회 논문지)

The Korea Institute of Intelligent Transport Systems (한국ITS학회)
권호 표지
DOI QR코드

DOI QR Code

Effects of Smartphone Usage on Walking Speed using Machine Learning Method

기계학습을 이용한 스마트폰 이용이 보행속도에 미치는 영향 분석

  • Jin, Hye ryun (Center of Infrastructure Asset Management, Hanbat National University) ;
  • Do, Myung sik (Dept. of Urban Eng., Hanbat National University)
  • 진혜련 (한밭대학교 SOC 자산관리센터) ;
  • 도명식 (한밭대학교 도시공학과)
  • Received : 2019.01.04
  • Accepted : 2019.03.04
  • Published : 2019.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

This study analyzed the impact of smartphone usage on walking speed during walking on two pedestrian walkways in Daejeon Metropolitan City. For the analysis, the video data about the actual use of smartphone was acquired and the walking speed was calculated based on the walking density of the pedestrian Level Of Service(LOS) presented in the Road Capacity Manual. Multiple regression analysis and decision tree using machine learning were used to analyze the impact of smartphone usage on walking speed, and as the explanatory variables, gender, disable smartphone, use of smartphone using auditory function, use of smartphone using visual function, LOS A, LOS B, LOS C were adopted. The result showed that LOS C had the highest impact on walking speed change and the women's group using their visual function was founded to have the slowest walking speed in LOS C. In particular, the author found that walking speed significantly decreased in the case of use of visual function rather than listening to music or the hearing on the phone.

본 연구에서는 대전광역시 내의 보행로 2개소를 대상으로 보행 중 스마트폰 사용이 보행속도에 미치는 영향을 분석하였다. 분석을 위해 스마트폰 사용실태에 대한 영상자료를 취득하고 도로용량편람에서 제시한 보행자 서비스수준의 보행밀도를 기준으로 보행속도를 산정하였다. 보행속도에 미치는 영향을 분석하기 위한 방법으로는 기계학습을 통한 다중회귀분석과 의사결정나무(Decision tree)를 활용하였으며, 설명변수로는 성별, 스마트폰 미사용, 청각을 이용한 스마트폰 사용, 시각을 이용한 스마트폰 사용, 서비스수준A, 서비스수준B, 서비스수준C가 선정되었다. 분석결과 서비스수준C가 보행속도 변화에 가장 높은 영향을 미치는 것으로 나타났으며, 서비스수준C에서 시각을 활용해 스마트폰을 이용한 여성그룹이 가장 낮은 속도로 보행하는 것으로 나타났다. 특히 음악 감상이나 통화와 같은 청각을 이용한 유형보다 시각을 이용하는 경우 대부분의 경우에서 보행속도가 크게 저하되며 통계적으로 유의한 차이가 있음을 확인하였다.

Keywords

References

  1. Choi J. H., Han S. T., Kang H. C. and Kim E. S.(1998), Data Mining Decision Tree Analysis Using Answer Tree, SPSS academy.
  2. Frenkel-Toledo N., Giladi C. and Peretz T.(2005), "Effect of gait speed on gait rhythmicity in Parkinson's disease: variability of stride time and swing time respond differently," J. of Neuroeng Rehabil., vol. 2, no. 23, pp.1-7. https://doi.org/10.1186/1743-0003-2-1
  3. Hyman Jr I. E., Boss S. M., Wise B. M., McKenzie K. E. and Caggiano J. M.(2010), "Pedestrians, vehicles, and cell phones," Accident Analysis and Prevention, vol. 42, pp.589-594. https://doi.org/10.1016/j.aap.2009.10.004
  4. Kang S. C., Lee S. W. and Shim J. I.(2016), "A study on patterns and distraction of Smart Devices Usages While Walking," J. of Transport Research, vol. 23, no. 2, pp.27-39. https://doi.org/10.34143/jtr.2016.23.2.27
  5. Lee H. Y. and Noh S. C.(2012), Advanced Statistical Analysis, Moon woo sa.
  6. Lee J. S. and Yang S. H.(2015), "The Effect of Using a Smartphone about Response Rate of the Body to the Visual and Auditory Stimuli During Walking," J. of the Korea Entertainment Industry Association, vol. 9, no. 3, pp.299-305.
  7. Ministry of Land, Infrastructure and Transport(2013), Korea Highway Capacity Manual.
  8. Muller C. A. and Guido S.(2016), Introduction Machine Learning with Python, Hanbit Media, Inc.
  9. Park G. Y.(2017), Risk Assessment and Prevention Plans on Drunk and Distracted Pedestrian Behavior, Samsung Traffic Safety Research Institute.
  10. Park H. W. and Park S. K.(2016), "Effects of Smart-phone Use on Gait Biomechanics and Auditory Cognition," J. of Sport and Leisure Studies, vol. 64, pp.783-795. https://doi.org/10.51979/KSSLS.2016.05.64.783
  11. Pellecchia G.(2005), "Dual-task Training reduces impact of cognitive task on postural sway," J. of Motor Behavior; Washington, vol. 37, no. 3, pp.239-246. https://doi.org/10.3200/JMBR.37.3.239-246
  12. Raschka S.(2016), Python Machine Learning, Packt Publishing.
  13. Rokach L. and Maimon O.(2015), Data Mining with Decision Trees, World Scientific.
  14. Schwebel D. C., Stavrinos D., Byington K. W., Davis T., O'Neal E. E. and de Jong D.(2012), "Distraction and Pedestrian Safety: How Talking in the Phone, Texting, and Listening to Music Impact Crossing the Street," Accident Analysis & Prevention, vol. 45, pp.266-271. https://doi.org/10.1016/j.aap.2011.07.011
  15. Shumway-Cook A. and Woollacott M.(2000), "Attentional demands and postural control: the effect of sensory context," J. of Gerontology-Biological Sciences and Medical Sciences, vol. 55, no. 1, pp. 10-16.
  16. Sim H. J., Do M. S. and Chong K. S.(2016), "Characteristic Analysis on Driver's Glance Durations with Different Running Speeds on the Expressway," J. of Korea Institute of Intelligent Transportation System, vol. 15, no. 1, pp.77-86.
  17. Thompson L. L., Rivara F. P., Ayyagari R. C. and Ebel B. E.(2012), "Impact of social and technological distraction on pedestrian crossing behavior; and observational study," Injury Prevention, vol. 19, pp.76-82. https://doi.org/10.1136/injuryprev-2012-040580d.10corr1
  18. You S. H. and Kwon C. H.(2017), "Effects of Smartphone Usage Patterns on Human Factors of Pedestrian Safety: Focused on Users in Their 20's and 30's," J. of Digital Convergence, vol. 15, no. 9, pp.79-85. https://doi.org/10.14400/JDC.2017.15.9.79