A personalized bikeability-based cycling route recommendation method with machine learning

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Original languageEnglish
Article number103373
Number of pages13
Journal / PublicationInternational Journal of Applied Earth Observation and Geoinformation
Online published12 Jun 2023
Publication statusPublished - Jul 2023



Urban built environment quality along the travel route is positively correlated with active mobility. However, most previous personalized route recommendation research for cycling emphasizes non-visual attributes, ignoring the significance of visual attributes such as the built environment. Therefore, to take the built environment into consideration in the cycling route planning, we propose the personalized bikeability-based cycling route recommendation (PBCRR) method. The method consists of three steps: (1) deep neural networks are utilized to achieve statistics on built environment attributes and features of bikeability; (2) the weights road impedance calculation function is used to calculate route recommendation indicators according to minimum impedance sum of road segments; and (3) the recommended cycling route is found and shown to the user based on the calculated indicators. The results indicate that our PBCRR presents a high level of effectiveness and differences, and achieves better results than the current route recommendation algorithm in guiding cyclists towards routes with a better built environment. © 2023 The Authors. Published by Elsevier B.V.

Research Area(s)

  • Active mobility, Urban built environment, Personalized travel route recommendation, Bikeability, Deep neural network

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