Name
Title | ||
|---|---|---|
A multi‐modal transportation data‐driven approach to identify urban functional zones: An exploration based on Hangzhou City, China |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1111/tgis.12591 | TRANSACTIONS IN GIS |
Field | DocType | Volume |
Data mining,Data-driven,Computer science,China,Transport engineering,Modal | Journal | 24.0 |
Issue | ISSN | Citations |
1.0 | 1361-1682 | 1 |
PageRank | References | Authors |
0.35 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhenhong Du | 1 | 31 | 16.98 |
| Xiaoyi Zhang | 2 | 1 | 0.35 |
| Wenwen Li | 3 | 321 | 26.87 |
| Feng Zhang | 4 | 1 | 0.35 |
| Liu Renyi | 5 | 15 | 13.13 |