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AllTheDocks road safety dataset: A cyclist's perspective and experience (2404.10528v1)

Published 16 Apr 2024 in cs.MM

Abstract: Active travel is an essential component in intelligent transportation systems. Cycling, as a form of active travel, shares the road space with motorised traffic which often affects the cyclists' safety and comfort and therefore peoples' propensity to uptake cycling instead of driving. This paper presents a unique dataset, collected by cyclists across London, that includes video footage, accelerometer, GPS, and gyroscope data. The dataset is then labelled by an independent group of London cyclists to rank the safety level of each frame and to identify objects in the cyclist's field of vision that might affect their experience. Furthermore, in this dataset, the quality of the road is measured by the international roughness index of the surface, which indicates the comfort of cycling on the road. The dataset will be made available for open access in the hope of motivating more research in this area to underpin the requirements for cyclists' safety and comfort and encourage more people to replace vehicle travel with cycling.

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References (11)
  1. M. Winters, R. Buehler, and T. Götschi, “Policies to promote active travel: evidence from reviews of the literature,” Current environmental health reports, vol. 4, pp. 278–285, 2017.
  2. European Commission, “(2023) facts and figures, mobility and amp; transport - road safety.” European Road Safety Observatory. Brussels, European Commission, Directorate General for Transport, Tech. Rep., 2023.
  3. J. Hagenauer and M. Helbich, “A comparative study of machine learning classifiers for modeling travel mode choice,” Expert Systems with Applications, vol. 78, pp. 273–282, 2017.
  4. P. B. Silva, M. Andrade, and S. Ferreira, “Machine learning applied to road safety modeling: A systematic literature review,” Journal of traffic and transportation engineering (English edition), vol. 7, no. 6, pp. 775–790, 2020.
  5. A. Paullada, I. D. Raji, E. M. Bender, E. Denton, and A. Hanna, “Data and its (dis) contents: A survey of dataset development and use in machine learning research,” Patterns, vol. 2, no. 11, 2021.
  6. D. Fraisl et al., “Citizen science in environmental and ecological sciences,” Nature Reviews Methods Primers, vol. 2, no. 1, p. 64, 2022.
  7. A. Sohail, M. A. Cheema, M. E. Ali, A. N. Toosi, and H. A. Rakha, “Data-driven approaches for road safety: A comprehensive systematic literature review,” Safety Science, vol. 158, p. 105949, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0925753522002880
  8. M. Costa, M. Marques, C. Roque, and F. Moura, “Cyclands: Cycling geo-located accidents, their details and severities,” Sci Data, vol. 9, p. 237, 2022. [Online]. Available: https://www.nature.com/articles/s41597-022-01333-2
  9. S. Cafiso, G. Pappalardo, and N. Stamatiadis, “Observed risk and user perception of road infrastructure safety assessment for cycling mobility,” Infrastructures, vol. 6, no. 11, 2021. [Online]. Available: https://www.mdpi.com/2412-3811/6/11/154
  10. E. Salcedo, M. Jaber, and J. Requena Carrión, “A novel road maintenance prioritisation system based on computer vision and crowdsourced reporting,” Journal of Sensor and Actuator Networks, vol. 11, no. 1, 2022. [Online]. Available: https://www.mdpi.com/2224-2708/11/1/15
  11. K. Zang, J. Shen, H. Huang, M. Wan, and J. Shi, “Assessing and mapping of road surface roughness based on gps and accelerometer sensors on bicycle-mounted smartphones,” Sensors, vol. 18, no. 3, 2018. [Online]. Available: https://www.mdpi.com/1424-8220/18/3/914

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