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Journal of Advanced Transportation
Volume 2017, Article ID 7871561, 12 pages
https://doi.org/10.1155/2017/7871561
Research Article

Influences of Waiting Time on Driver Behaviors While Implementing In-Vehicle Traffic Light for Priority-Controlled Unsignalized Intersections

1Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, Japan
2Interfaculty Initiative in Information Studies, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, Japan

Correspondence should be addressed to Bo Yang; pj.ca.oykot-u.sii@gnay-b

Received 12 May 2017; Revised 3 September 2017; Accepted 14 September 2017; Published 17 October 2017

Academic Editor: Chunjiao Dong

Copyright © 2017 Bo Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. National Highway Traffic Safety Administration, Fatality Analysis and Reporting System, U. S. Department of Transportation, Washington, D. C., USA, 2014.
  2. B. Yang, R. Zheng, K. Shimono, T. Kaizuka, and K. Nakano, “Evaluation of the effects of in-vehicle traffic lights on driving performances for unsignalised intersections,” IET Intelligent Transport Systems, vol. 11, no. 2, pp. 76–83, 2017. View at Publisher · View at Google Scholar · View at Scopus
  3. B. Yang, R. Zheng, and K. Nakano, “Application of in-vehicle traffic lights for improvement of driving safety at unsignalized intersections,” in Proceedings of the IEEE Intelligent Vehicles Symposium, IV 2015, pp. 628–633, Seoul, Korea, July 2015. View at Publisher · View at Google Scholar · View at Scopus
  4. D. Max, S. Craig, J. W. Nicolas, and C. Janet, Intersection Decision Support: an Overview Report #6 in the Series: Developing Intersection Decision Support Solutions, University of Minnesota, 2007.
  5. R. David, A. Sofia, E. S. Steven, A. M. James, and Y. C. Ching, “Gap acceptance for vehicles turning left across on-coming traffic: implications for intersection decision support design,” in Proceedings of the Transportation Research Board 85th Annual Meeting, pp. 1–25, Washington, D. C., USA, 2006.
  6. National Highway Traffic Safety Administration, Traffic Safety Facts 2014, U.S. Department of Transportation, Washington, D. C., USA, 2016.
  7. National Cooperative Highway Research Program, NCHRP Report 500, Vol. 5: a Guide for Addressing Unsignalized Intersection Collisions, Transportation Research Board, 2003.
  8. J.-A. Jang, K. Choi, and H. Cho, “A fixed sensor-based intersection collision warning system in vulnerable line-of-sight and/or traffic-violation-prone environment,” IEEE Transactions on Intelligent Transportation Systems, vol. 13, no. 4, pp. 1880–1890, 2012. View at Publisher · View at Google Scholar · View at Scopus
  9. J. Fischer, A. Menon, A. Gorjestani, C. Shankwitz, and M. Donath, “Range sensor evaluation for use in cooperative intersection collision avoidance systems,” in Proceedings of the 2009 IEEE Vehicular Networking Conference, VNC 2009, Tokyo, Japan, October 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. M. P. Van, “Correlation of design and control characteristics with accidents at rural multi-lane highway intersections in Indiana: interim report,” Tech. Rep. FHWA/IN/JHRP-77/22, Joint Highway Research Project, Indiana Department of Transportation and Purdue University, West Lafayette, Ind, USA, 1977. View at Google Scholar
  11. T. Streubel and K. H. Hoffmann, “Prediction of driver intended path at intersections,” in Proceedings of the 25th IEEE Intelligent Vehicles Symposium (IV '14), pp. 134–139, IEEE, Dearborn, Mich, USA, June 2014. View at Publisher · View at Google Scholar · View at Scopus
  12. G. Burchett and T. Maze, “Rural expressway intersections that contribute to reduced safety performance,” in Proceedings of the Mid-Continent Transportation Research Symposium, pp. 1–14, Ames, Iowa, USA, 2005.
  13. O. K. Tonguz, “Notice of violation of IEEE publication principles biologically inspired solutions to fundamental transportation problems,” IEEE Communications Magazine, vol. 490, no. 11, pp. 106–115, 2011. View at Publisher · View at Google Scholar
  14. M. Ferreira, R. Fernandes, H. Conceição, W. Viriyasitavat, and O. K. Tonguz, “Self-organized traffic control,” in Proceedings of the 7th ACM International Workshop on VehiculAr InterNETworking (VANET '10), pp. 85–89, Chicago, Ill, USA, September 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. B. Yang, R. Zheng, Y. Yin, S. Yamabe, and K. Nakano, “Analysis of influence on driver behaviour while using in-vehicle traffic lights with application of head-up display,” IET Intelligent Transport Systems, vol. 10, no. 5, pp. 347–353, 2016. View at Publisher · View at Google Scholar · View at Scopus
  16. M. Ferreira and P. M. D'Orey, “On the impact of virtual traffic lights on carbon emissions mitigation,” IEEE Transactions on Intelligent Transportation Systems, vol. 13, no. 1, pp. 284–295, 2012. View at Publisher · View at Google Scholar · View at Scopus
  17. C. O. Monreal, P. Gomes, M. K. Silvéria, and M. Ferreira, “In-vehicle virtual traffic lights, a graphical user interface,” in Proceedings of the 7th Iberian Conference on Information Systems and Technologies, pp. 1–6, Madrid, Spain, 2012.
  18. H. Conceicao, M. Ferreira, and P. Steenkiste, “Virtual traffic lights in partial deployment scenarios,” in Proceedings of the 2013 IEEE Intelligent Vehicles Symposium, IEEE IV 2013, pp. 988–993, Gold Coast, Australia, June 2013. View at Publisher · View at Google Scholar · View at Scopus
  19. Transportation Research Board, “Highway Capacity Manual,” fifth edition, Washington, D. C., USA, pp. 1-1650, 2010.
  20. J. M. Bunker, “Novel methods and the maximum likelihood estimation technique for estimating traffic critical gap,” Journal of Advanced Transportation, vol. 48, no. 6, pp. 542–555, 2014. View at Publisher · View at Google Scholar · View at Scopus
  21. K. Fitzpatrick, “Gaps accepted at stop-controlled intersections,” Transportation Research Record, vol. 1303, no. 11, pp. 103–112, 1991. View at Google Scholar
  22. S. M. Tupper, M. A. Knodler, and D. S. Hurwitz, “Connecting gap acceptance behavior with crash experience,” in Proceedings of the 3rd International Conference on Road Safety and Simulation, pp. 1–18, Indianapolis, Ind, USA, 2011.
  23. J. Thakonlaphat and S. Kazushi, “Effect of waiting time on the gap acceptance behavior of u-turning vehicles at midblock median openings,” Journal of the Eastern Asia Society for Transportation Studies, vol. 9, pp. 1601–1613, 2011. View at Google Scholar
  24. I. A. O. Turki and S. E. Mohammad, “Gap acceptance behavior at u-turn median openings – case study in Jordan,” Jordan Journal of Civil Engineering, vol. 7, no. 3, pp. 332–341, 2013. View at Google Scholar
  25. S. Nabaee, An Evaluation of Gap Acceptance Behavior at Unsignalized Intersections, Oregon State University, 2011.
  26. S. M. Tupper, Safety and Operational Assessment of Gap Acceptance through Large-Scale Field Evaluation, University of Massachusetts Amherst, 2014.
  27. “Japan Automobile Manufacturers Association,” Guidelines for In-Vehicle Display Systems–Version 3.0, pp. 1–15, Tokyo, Japan, 2004.
  28. W. K. M. Alhajyaseen, “The development of conflict index for the safety assessment of intersections considering crash probability and severity,” in Proceedings of the 5th International Conference on Ambient Systems, Networks and Technologies, ANT 2014 and 4th International Conference on Sustainable Energy Information Technology, SEIT 2014, pp. 364–371, bel, June 2014. View at Publisher · View at Google Scholar · View at Scopus
  29. Indicators for traffic safety assessment and prediction and their application in micro-simulation modelling: a study of urban and suburban intersections [ph.D thesis], Royal Institute of Technology, 2005.
  30. L. Peesapati, M. Hunter, and M. Rodgers, “Evaluation of postencroachment time as surrogate for opposing left-turn crashes,” Transportation Research Record, no. 2386, pp. 42–51, 2013. View at Publisher · View at Google Scholar · View at Scopus
  31. E. Roidl, B. Frehse, and R. Höger, “Emotional states of drivers and the impact on speed, acceleration and traffic violations - a simulator study,” Accident Analysis & Prevention, vol. 70, pp. 282–292, 2014. View at Google Scholar
  32. A. N. Stephens and J. A. Groeger, “Situational specificity of trait influences on drivers' evaluations and driving behaviour,” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 12, no. 1, pp. 29–39, 2009. View at Publisher · View at Google Scholar · View at Scopus
  33. D. Waard, K. Brookhuis, and A. Toffetti, Developments in Human Factors in Transportation, Design, and Evaluation, Shaker Publishing, Maastricht, the Netherlands, 2006.
  34. Y.-C. Lee and F. K. Winston, “Stress induction techniques in a driving simulator and reactions from newly licensed drivers,” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 42, pp. 44–55, 2016. View at Publisher · View at Google Scholar · View at Scopus
  35. H. S. Friedman and M. W. Schustack, Personality: classic Theories and Modern Research, Pearson, London, UK, 5th edition, 2010.
  36. G. M. Björklund, “Driver irritation and aggressive behaviour,” Accident Analysis & Prevention, vol. 40, no. 3, pp. 1069–1077, 2008. View at Google Scholar
  37. S. Thibaud, K. Rana, and H. Niels, “Mental state analysis using blink rate,” Article ID 20140200417, US patent, US 20140200417 A1, 2014.
  38. H. Siiri, Neural responses to observed eye blinks in normal and slow motion: an MEG study [m.s. Thesis], University of Helsinki, 2012.
  39. B. R. N. Ramachandran, P. S. A. Romero, J. Born, S. Winkler, and R. Ratnam, “Measuring neural, physiological and behavioral effects of frustration,” in Proceedings of the 16th International Conference on Biomedical Engineering, pp. 43–46, Singapore, 2016.
  40. M. J. Doughty and T. Naase, “Further analysis of the human spontaneous eye blink rate by a cluster analysis-based approach to categorize individuals with 'normal' versus 'frequent' eye blink activity,” Fırat Sağlık Hizmetleri Dergisi, vol. 32, no. 6, pp. 294–299, 2006. View at Publisher · View at Google Scholar · View at Scopus
  41. K. Fukuda, “Eye blinks: New indices for the detection of deception,” International Journal of Psychophysiology, vol. 40, no. 3, pp. 239–245, 2001. View at Publisher · View at Google Scholar · View at Scopus
  42. M. Al-Abdulmunem and S. T. Briggs, “Spontaneous blink rate of a normal population sample,” International Contact Lens Clinic, vol. 26, no. 2, pp. 29–32, 1999. View at Publisher · View at Google Scholar · View at Scopus
  43. J. Oh, S.-Y. Jeong, and J. Jeong, “The timing and temporal patterns of eye blinking are dynamically modulated by attention,” Human Movement Science, vol. 31, no. 6, pp. 1353–1365, 2012. View at Publisher · View at Google Scholar · View at Scopus
  44. S. Classen, O. Shechtman, B. Stephens et al., “The impact of roadway intersection design on driving performance of young and senior adults,” Traffic Injury Prevention, vol. 8, no. 1, pp. 69–77, 2007. View at Publisher · View at Google Scholar · View at Scopus
  45. J. K. Caird, S. L. Chisholm, C. J. Edwards, and J. I. Creaser, “The effect of yellow light onset time on older and younger drivers' perception response time (PRT) and intersection behavior,” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 10, no. 5, pp. 383–396, 2007. View at Publisher · View at Google Scholar · View at Scopus
  46. D. V. McGehee, E. N. Mazzae, G. H. S. Baldwin et al., “Examination of drivers’ collision avoidance behavior using conventional and antilock brake systems on the Iowa driving simulator,” National Highway Traffic Safety Administration, pp. 1–102, 1999, Washington, D. C., USA. View at Google Scholar
  47. E. N. Mazzae, F. S. Barickman, G. Forkenbrock, G. H. Baldwin, and S., “NHTSA light vehicle antilock brake system research program task 5.2/5.3: test track examination of drivers’ collision avoidance behavior using conventional and antilock brakes,” National Highway Traffic Safety Administration, pp. 1–161, 2003. View at Google Scholar
  48. S. G. Roberts and T. D. Day, “Integrating design and virtual test environments for brake component design and material selection,” SAE Technical Papers, 2000. View at Publisher · View at Google Scholar · View at Scopus
  49. A. N. Stephens and M. Fitzharris, “Validation of the driver behaviour questionnaire in a representative sample of drivers in Australia,” Accident Analysis & Prevention, vol. 86, pp. 186–198, 2016. View at Google Scholar
  50. F. Sagberg, “Road accidents caused by drivers falling asleep,” Accident Analysis & Prevention, vol. 31, no. 6, pp. 639–649, 1999. View at Google Scholar
  51. B. Woodrow and A. D. Thomas, Human Factors in Intelligent Transportation Systems, Psychology Press, 1st edition, 1997.
  52. M. Kumar and G. S. Rao, Statistical Techniques for Transportation Engineering, Butterworth-Heinemann, 1st edition, 2017.
  53. E. Bradley, Research and Evaluation in Counseling (Research, Statistics, &Amp; Program Evaluation), Wadsworth Publishing, 1st edition, 2007.
  54. S. Chow, J. Shao, and H. Wang, Sample Size Calculations in Clinical Research, Chapman & Hall/CRC Biostatistics Series, 2nd edition, 2007.
  55. J. Cohen, “A power primer,” Psychological Bulletin, vol. 112, no. 1, pp. 155–159, 1992. View at Publisher · View at Google Scholar · View at Scopus
  56. B. M. Scott, “Sample size required for adverse impact analysis,” Applied HRM Research, vol. 6, no. 1, pp. 13–32, 2001. View at Google Scholar