Deriving Real-time City Crowd Flows by Heterogeneous Big Urban Data

Tang, Bo; Yang, Chuan; Xiang, Long; Zeng, Jian

10.1109/BigData.2018.8622370

2018

2639-1589

978-1-5386-5036-3

2018 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)

3485-3494

10-13 Dec. 2018

Seattle, WA, United states

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

Real-time city crowd flows are extremely important for facility placement, transportation management, and public safety. In this paper, we show how to derive the real-time city crowd flows with heterogeneous urban data. Unlike existing prediction-based approaches, our proposal does not rely on the training data and learning models. We propose a computation framework for it by exploiting the massive heterogeneous urban data, which includes both immutable data (i.e., bus and subway stations, commercial-based regions) and mutable data (i.e., real-time taxi, bus, subway transactions and trajectories). In addition, our solution provides accurate and timely city crowd-flows.

Real-time crowd flows Heterogeneous spatial-temporal data

第一 ; 通讯

英语

CPCI-S ; EI

Program for University Key Laboratory of Guangdong Province[2017KSYS008]

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Computer Science, Theory & Methods

WOS:000468499303073

20191106616420

Big data ; Data fusion ; Railroads ; Subway stations ; Taxicabs

Passenger Railroad Transportation:433.2 ; Automobiles:662.1 ; Data Processing and Image Processing:723.2

Web of Science

Southern Univ Sci & Technol, Dept Comp Sci & Engn, Univ Key Lab Evolving Intelligent Syst Guangdong, Shenzhen Key Lab Computat Intelligence, Shenzhen, Peoples R China

Tang, Bo,Yang, Chuan,Xiang, Long,et al. Deriving Real-time City Crowd Flows by Heterogeneous Big Urban Data[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2018:3485-3494.