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Cycling comfort evaluation with instrumented probe bicycle

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  • Zhu, Siying
  • Zhu, Feng

Abstract

The cycling comfort level of different cycling infrastructure can strongly influence the comfort perception of cyclists and their route choices. In this paper, the cycling comfort index (CCI) is used to measure the cycling comfort level on cycling infrastructure and describe different cycle track characteristics. An Instrumented Probe Bicycle (IPB), which is equipped with a video camera and a set of sensors including GPS receiver, accelerometer, etc., is employed to collect data while being ridden by cyclist in Singapore. An automatic video processing technique using convolutional neural network (CNN) is applied, such that no direct field measurement is required and the data collection process is less time-consuming. Video-based survey is carried out to capture the correlation between CCI and the comfort perception of cyclists. The extreme gradient boosting (XGBoost) method is employed to build the CCI model dependent on various explanatory variables and survey participants’ ratings. The results show that the overall accuracy of the XGBoost method is 11% higher than the ordered Probit model commonly used in literature.

Suggested Citation

  • Zhu, Siying & Zhu, Feng, 2019. "Cycling comfort evaluation with instrumented probe bicycle," Transportation Research Part A: Policy and Practice, Elsevier, vol. 129(C), pages 217-231.
  • Handle: RePEc:eee:transa:v:129:y:2019:i:c:p:217-231
    DOI: 10.1016/j.tra.2019.08.009
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    1. Bai, Lu & Liu, Pan & Chan, Ching-Yao & Li, Zhibin, 2017. "Estimating level of service of mid-block bicycle lanes considering mixed traffic flow," Transportation Research Part A: Policy and Practice, Elsevier, vol. 101(C), pages 203-217.
    2. Tilahun, Nebiyou Y. & Levinson, David M. & Krizek, Kevin J., 2007. "Trails, lanes, or traffic: Valuing bicycle facilities with an adaptive stated preference survey," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(4), pages 287-301, May.
    3. Ding, Chuan & Cao, Xinyu (Jason) & Næss, Petter, 2018. "Applying gradient boosting decision trees to examine non-linear effects of the built environment on driving distance in Oslo," Transportation Research Part A: Policy and Practice, Elsevier, vol. 110(C), pages 107-117.
    4. Calvey, J.C. & Shackleton, J.P. & Taylor, M.D. & Llewellyn, R., 2015. "Engineering condition assessment of cycling infrastructure: Cyclists’ perceptions of satisfaction and comfort," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 134-143.
    5. Zohreh Asadi-Shekari & Mehdi Moeinaddini & Muhammad Zaly Shah, 2013. "Non-motorised Level of Service: Addressing Challenges in Pedestrian and Bicycle Level of Service," Transport Reviews, Taylor & Francis Journals, vol. 33(2), pages 166-194, March.
    6. Griswold, Julia B. & Yu, Mengqiao & Filingeri, Victoria & Grembek, Offer & Walker, Joan L., 2018. "A behavioral modeling approach to bicycle level of service," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 166-177.
    7. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    8. Joo, Shinhye & Oh, Cheol, 2013. "A novel method to monitor bicycling environments," Transportation Research Part A: Policy and Practice, Elsevier, vol. 54(C), pages 1-13.
    9. Daniel J Fagnant & Kara Kockelman, 2016. "A direct-demand model for bicycle counts: the impacts of level of service and other factors," Environment and Planning B, , vol. 43(1), pages 93-107, January.
    10. Ralph Buehler & Jennifer Dill, 2016. "Bikeway Networks: A Review of Effects on Cycling," Transport Reviews, Taylor & Francis Journals, vol. 36(1), pages 9-27, January.
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    2. Tufail Ahmed & Ali Pirdavani & Davy Janssens & Geert Wets, 2023. "Utilizing Intelligent Portable Bicycle Lights to Assess Urban Bicycle Infrastructure Surfaces," Sustainability, MDPI, vol. 15(5), pages 1-22, March.

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