Nonlinear Influence of Urban Environment on Dockless Shared Bicycle Travel Patterns

In response to the lack of ability to handle multidimensional data in current research methods for shared bicycle travel patterns, and the fact that correlation analysis is only conducted on a single feature, this study investigates the travel pattern using tensor decomposition and a random forest model. Based on the riding data of dockless shared bicycles in Shenzhen, tensor decomposition is applied to extract three shared bicycle travel patterns: peak-high traffic pattern, steady traffic pattern, and off-peak high traffic pattern. Spatially, each pattern exhibits clustering, and the travel volume decreases from the center to the periphery. Based on this, with 13 built environment factors as feature variables, a random forest model is trained. Importance and interaction analyses are performed for both individual features and feature combinations. The results indicate that the random forest model demonstrates excellent fitting performance and accuracy. Furthermore, for the peak-high traffic pattern, the combination of the length of primary roads and the number of companies contributes the most, while for the steady traffic pattern, it is the combination of the number of malls and companies. Finally, for the off-peak high traffic pattern, the influence of the number of malls and interests is the most significant.