Let's Connect
Follow Us
Watch Us
(+385) 1 2380 262
journal.prometfpz.unizg.hr
Promet - Traffic&Transportation journal

Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

Sign In or Register Submit

PDF
PUBLISHED
31.10.2024
DOI
https://doi.org/10.7307/ptt.v36i5.534
ISSUE
2024 (Vol 36), Issue 5
LICENSE
Copyright (c) 2024 Baojie WANG, Wei YANG, Ziyao LIU, Guohua LIANG
Creative Commons 4.0 logo
This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

Implications of Retrograde Behaviours on Visual and Cycling Behaviour of Normal Cyclists

Authors:Baojie WANG, Wei YANG, Ziyao LIU, Guohua LIANG

Abstract

In China and other developing countries, some bicycle riders exhibit retrograde behaviour, which affects the riding safety of normal cyclists. The effect of retrograde behaviour on visual search and cycling behaviours of normal cyclists is investigated and quantified in this study. First, cyclists are instructed to wear an SMI iView ETG head-mounted mobile eye tracker and a mobile phone equipped with a Global Positioning System real-time location monitoring function to cycle on a road to obtain the times of fixation, saccade and blink, as well as the pupil diameter, gaze position and velocity in normal and retrograde conditions. Subsequently, the effect of retrograde behaviour on the attention of normal cyclists is analysed using three indexes: proportion of fixation time, coefficient of variation of pupil diameter and area of interest. Then, the effect of cycling behaviour is analysed using three indexes: the cycling trajectory, the velocity at three stages and the coefficient of variation of velocity. Finally, polynomial regression analysis is performed to analyse the visual and cycling behaviour impact indexes under the retrograde condition. The results show that retrograde behaviour significantly affects the vision and cycling behaviour of normal cyclists and that the two indexes are positively correlated.

Keywords:visual behaviour, cycling behaviour, traffic safety, retrograde behaviour

References

  1. [1] Fan A, Chen X, Wan T. How have travelers changed mode choices for first/last mile trips after the introduction of bicycle-sharing systems: an empirical study in Beijing, China. Journal of Advanced Transportation. 2019. DOI: 10.1155/2019/5426080.
  2. [2] Gunterson K. Countermeasures prove effective in reducing bicycle collisions. Institute of Transportation Engineers. 2018;88(5):29–33.
  3. [3] Dozza M, Werneke J. Introducing naturalistic cycling data: What factors influence bicyclists’ safety in the real world?. Transportation Research Part F: Traffic Psychology and Behaviour. 2014;24:3–91. DOI: 10.1016/j.trf.2014.04.001.
  4. [4] Siamidoudaran M, Iscioglu E. Injury severity prediction of traffic collision by applying a series of neural networks: The city of London case study. Promet – Traffic & Transportation. 2019;31(6):643–654. DOI: 10.7307/ptt.v31i6.3032.
  5. [5] Loo BP, Tsui KL. Bicycle crash casualties in a highly motorized city. Accident Analysis & Prevention. 2010;42(6):1902–1907. DOI: 10.1016/j.aap.2010.05.011.
  6. [6] Raihan MA, Alluri P. Impact of roadway characteristics on bicycle safety. Institute of Transportation Engineers. 2017;87(9):33.
  7. [7] Yan X, et al. Motor vehicle–bicycle crashes in Beijing: Irregular maneuvers, crash patterns, and injury severity. Accident Analysis & Prevention. 2011;43(5):1751–1758. DOI:10.1016/j.aap.2011.04.006.
  8. [8] Schepers P, den Brinker B. What do cyclists need to see to avoid single-bicycle crashes?. Ergonomics. 2011;54(4):315–327. DOI: 10.1080/00140139.2011.558633.
  9. [9] Oketch, TG. New modeling approach for mixed-traffic streams with nonmotorized vehicles. Transportation research record. 2000;1705(1):61–69. DOI: 10.3141/1705-10.
  10. [10] Wang H, Si H, Wang X. Cyclist’s intention identification on pedestrian-bicycle mixed sections based on phase-field coupling theory. Promet – Traffic & Transportation. 2019;31(3):233–244. DOI: 10.7307/ptt.v31i3.2927.
  11. [11] Botma H, Papendrecht H. Traffic operation of bicycle traffic. Transportation Research Record. 1991;(1320).
  12. [12] Parkin J, Meyers C. The effect of cycle lanes on the proximity between motor traffic and cycle traffic. Accident Analysis & Prevention. 2010;42(1):159–165. DOI: 10.1016/j.aap.2009.07.018.
  13. [13] Jin S, et al. Modelling speed–flow relationships for bicycle traffic flow. In Proceedings of the Institution of Civil Engineers-Transport. 2017;170(4):194–204. DOI: 10.1680/jtran.15.00115.
  14. [14] Lin A, Lou J. Pedestrians’ and cyclists’ preferences for street greenscape designs. Promet – Traffic & Transportation. 2022;34(3):367–380. DOI: 10.7307/ptt.v34i3.3879.
  15. [15] Dong P, et al. Research on the characteristics of mixed traffic flow based on an improved bicycle model. Simulation. 2018;94(5):451–462. DOI: 10.1177/0037549717736947.
  16. [16] Mohammed H, Bigazzi AY, Sayed T. Characterization of bicycle following and overtaking maneuvers on cycling paths. Transportation research part C: emerging technologies. 2019;98:139–151. DOI: 10.1016/j.trc.2018.11.012.
  17. [17] Langford BC, Chen J, Cherry, CR. Risky riding: Naturalistic methods comparing safety behavior from conventional bicycle riders and electric bike riders. Accident Analysis & Prevention. 2015;82:220–226. DOI: 10.1016/j.aap.2015.05.016.
  18. [18] Tang TQ, et al. Modeling electric bicycle’s lane-changing and retrograde behaviors. Physica A: Statistical Mechanics and Its Applications. 2018;490:1377–1386. DOI: 10.1016/j.physa.2017.08.107.
  19. [19] Wierbos MJ, et al. A macroscopic flow model for mixed bicycle–car traffic. Transportmetrica A: transport science. 2021;17(3):340–355. DOI: 10.1080/23249935.2019.1708512.
  20. [20] Wang C, et al. Exploring factors influencing the risky cycling behaviors of young cyclists aged 15–24 years: a questionnaire‐based study in China. Risk Analysis. 2020;40. DOI: 10.1111/risa.13499.
  21. [21] Useche SA, et al. Distraction of cyclists: How does it influence their risky behaviors and traffic crashes?. PeerJ. 2018;6(1):e5616. DOI: 10.7717/peerj.5616.
  22. [22] Cheng WA, et al. Aberrant behaviours in relation to the self-reported crashes of bicyclists in China: Development of the Chinese cycling behaviour questionnaire. Transportation Research Part F: Traffic Psychology and Behaviour. 2019;66:63–75. DOI: 10.1016/j.trf.2019.08.022.
  23. [23] Twisk D, et al. Relationships amongst psychological determinants, risk behaviour, and road crashes of young adolescent pedestrians and cyclists: Implications for road safety education programmes. Transportation Research Part F: Traffic Psychology and Behaviour. 2015;30(Apr.):45–56. DOI: 10.1016/j.trf.2015.01.011.
  24. [24] Engström J, Johansson E, Östlund J. Effects of visual and cognitive load in real and simulated motorway driving. Transportation Research Part F: Traffic Psychology and Behaviour. 2005;8(2):97–120. DOI: 10.1016/j.trf.2005.04.012.
  25. [25] Zeuwts L, et al. Is gaze behaviour in a laboratory context similar to that in real-life? A study in bicyclists. Transportation Research Part F: Traffic Psychology and Behaviour. 2016;43:131–140. DOI: 10.1016/j.trf.2016.10.010.
  26. [26] Vansteenkiste P, et al. The visual control of bicycle steering: The effects of speed and path width. Accident Analysis & Prevention. 2013;51:222–227. DOI: 10.1016/j.aap.2012.11.025.
  27. [27] Vansteenkiste P, et al. The implications of low quality bicycle paths on gaze behavior of cyclists: A field test. Transportation Research Part F: Traffic Psychology and Behaviour. 2014;23:81-87. DOI: 10.1016/j.trf.2013.12.019.
  28. [28] Kovacsova N, et al. Cyclists’ eye movements and crossing judgments at uncontrolled intersections: An eye-tracking study using animated video clips. Accident Analysis & Prevention. 2018;120:270–280. DOI: 10.1016/j.aap.2018.08.024.
  29. [29] Borowsky A, Oron-Gilad T, Parmet Y. Age and skill differences in classifying hazardous traffic scenes. Transportation Research Part F: Traffic Psychology and Behaviour. 2009;12(4):277–287. DOI: 10.1016/j.trf.2009.02.001.
  30. [30] Zeuwts LH, et al. Hazard perception in young cyclists and adult cyclists. Accident Analysis & Prevention. 2017;105:64–71. DOI: 10.1016/j.aap.2016.04.034.
  31. [31] Melin MC, et al. Where do people direct their attention while cycling? A comparison of adults and children. Transportation Research Part F: Traffic Psychology and Behaviour. 2018;58:292–301. DOI: 10.1016/j.trf.2018.06.017.
  32. [32] Trefzger M, et al. A visual comparison of gaze behavior from pedestrians and cyclists. In Proceedings of the 2018 ACM symposium on eye tracking research & applications, 2018, 14-17 Jun. 2018, Warsaw, Poland. 2018. p. 1–5. DOI: 10.1145/3204493.3204553.
  33. [33] Mantuano A, Bernardi S, Rupi F. Cyclist gaze behavior in urban space: An eye-tracking experiment on the bicycle network of Bologna. Case studies on transport policy. 2017;5(2):408–416. DOI: 10.1016/j.cstp.2016.06.001.
  34. [34] Stelling-Konczak A, et al. A study in real traffic examining glance behaviour of teenage cyclists when listening to music: results and ethical considerations. Transportation Research Part F: Traffic Psychology and Behaviour. 2018;55:47–57. DOI: 10.1016/j.trf.2018.02.031.
  35. [35] Patla AE. Understanding the roles of vision in the control of human locomotion. Gait & posture. 1997;5(1):54–69. DOI: 10.1016/S0966-6362(96)01109-5.
  36. [36] Godley ST, Triggs TJ, Fildes BN. Perceptual lane width, wide perceptual road centre markings and driving speeds. Ergonomics. 2004;47(3):237–256. DOI: 10.1080/00140130310001629711.
  37. [37] Kübler TC, Kasneci E, Vintila F. Pupil response as an indicator of hazard perception during simulator driving. Journal of Eye Movement Research. 2017;10(4). DOI: 10.16910/jemr.10.4.3.
  38. [38] Niu J, et al. Study on drivers' visual perception characteristics during the take-over of vehicle control in automated driving. Human Factors and Ergonomics in Manufacturing. 2020;(5). DOI: 10.1002/hfm.20860.
  39. [39] Ahlstrom C, et al. Bicyclists’ visual strategies when conducting self-paced vs. system-paced smartphone tasks in traffic. Transportation Research Part F: Traffic Psychology and Behaviour. 2016;41:204–216. DOI: 10.1016/j.trf.2015.01.010.
  40. [40] Young AH, Hulleman J. Eye movements reveal how task difficulty moulds visual search. Journal of Experimental Psychology: Human Perception and Performance. 2013;39(1):168. DOI: 10.1037/a0028679.
  41. [41] Zhu W, Zhai B, Jian D. Evaluating the bicycle travel environment in a changing bicycle culture: Case study of Shanghai, China. Journal of Urban Planning and Development. 2017;143(3):05017001. DOI: 10.1061/(ASCE)UP.1943-5444.0000377.
  42. [42] Werneke J, Dozza M, Karlsson M. Safety–critical events in everyday cycling–Interviews with bicyclists and video annotation of safety–critical events in a naturalistic cycling study. Transportation Research Part F: Traffic Psychology and Behaviour. 2015;35:199–212. DOI: 10.1016/j.trf.2015.10.004.
  43. [43] Itti L, Koch C. Computational modelling of visual attention. Nature reviews neuroscience. 2001;2(3):194–203. DOI: 10.1038/35058500.
  44. [44] Posner MI. Orienting of attention. Quarterly journal of experimental psychology. 1980;32(1):3–25. DOI: 10.1080/00335558008248231.
Show more

SPECIAL ISSUE CALL

Innovation and New Technologies in Transport and Logistics

Guest Editor: Eleonora Papadimitriou, PhD

Editors: Dario Babić, PhD; Marko Matulin, PhD; Marko Ševrović, PhD.

RECENT PAPERS

Browse through the selection of our recent research
Bibliometric Analysis of Traffic Accident Prediction Studies from 2003 to 2023: Trends, Patterns and Future DirectionsResearch on the Efficiency of Major Airports in China’s Six Major Airport Cluster: Based on Common Frontier Super-Efficiency DEA and Malmquist MethodCollege Students’ Cognition and Attitude Towards Connected and Autonomous Vehicles in China: an Exploratory StudyEstimating Vehicle Turn-In Rate of Expressway Rest Areas via ETC Gantry Data – An ADPC-GMM Approach

Accelerating Discoveries in Traffic Science |
2024 © Promet - Traffic&Transportation journal