Recent urban studies have used human mobility data such as taxi trajectories and smartcard data as a complementary way to identify the social functions of land use. However, little work has been conducted to reveal how multi-modal transportation data impact on this identification process. In our study, we propose a data-driven approach that addresses the relationships between travel behavior and urban structure: first, multi-modal transportation data are aggregated to extract explicit statistical features; then, topic modeling methods are applied to transform these explicit statistical features into latent semantic features; and finally, a classification method is used to identify functional zones with similar latent topic distributions. Two 10-day-long “big” datasets from the 2,370 bicycle stations of the public bicycle-sharing system, and up to 9,992 taxi cabs within the core urban area of Hangzhou City, China, as well as point-of-interest data are tested to reveal the extent to which different travel modes contribute to the detection and understanding of urban land functions. Our results show that: (1) using latent semantic features delineated from the topic modeling process as the classification input outperforms approaches using explicit statistical features; (2) combining multi-modal data visibly improves the accuracy and consistency of the identified functional zones; and (3) the proposed data-driven approach is also capable of identifying mixed land use in the urban space. This work presents a novel attempt to uncover the hidden linkages between urban transportation patterns with urban land use and its functions.
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)