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

Abstract

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.