Understanding Metropolitan Crowd Mobility via Mobile Cellular Accessing Data
| Autor: | Hanan Samet, Yong Li, Jie Feng, Jagan Sankaranarayanan, Hancheng Cao |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Structure (mathematical logic)
City block Computer science Mobile broadband Metropolitan area Data science Computer Science Applications Mobility analysis Modeling and Simulation Urban computing Signal Processing Discrete Mathematics and Combinatorics Geometry and Topology Information Systems TRACE (psycholinguistics) |
| Zdroj: | ACM Transactions on Spatial Algorithms and Systems. 5:1-18 |
| ISSN: | 2374-0361 2374-0353 |
| Popis: | Understanding crowd mobility in a metropolitan area is extremely valuable for city planners and decision makers. However, crowd mobility is a relatively new area of research and has significant technical challenges: lack of large-scale fine-grained data, difficulties in large-scale trajectory processing, and issues with spatial resolution. In this article, we propose a novel approach for analyzing crowd mobility on a “city block” level. We first propose algorithms to detect homes, working places, and stay regions for individual user trajectories. Next, we propose a method for analyzing commute patterns and spatial correlation at a city block level. Using mobile cellular accessing trace data collected from users in Shanghai, we discover commute patterns, spatial correlation rules, as well as a hidden structure of the city based on crowd mobility analysis. Therefore, our proposed methods contribute to our understanding of human mobility in a large metropolitan area. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|