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Urban flow prediction from spatiotemporal data using machine learning: A survey.

Authors :
Xie, Peng
Li, Tianrui
Liu, Jia
Du, Shengdong
Yang, Xin
Zhang, Junbo
Source :
Information Fusion. Jul2020, Vol. 59, p1-12. 12p.
Publication Year :
2020

Abstract

• Urban flow prediction from spatiotemporal data is systematically reviewed. • Some existing urban flow prediction methods based on machine learning are overviewed. • The difficulties and some ideas of urban flow prediction are discussed briefly. Urban spatiotemporal flow prediction is of great importance to traffic management, land use, public safety. This prediction task is affected by several complex and dynamic factors, such as patterns of human activities, weather, events, and holidays. Datasets evaluated the flow come from various sources in different domains, e.g. mobile phone data, taxi trajectories data, metro/bus swiping data, bike-sharing data. To summarize these methodologies of urban flow prediction, in this paper, we first introduced four main factors affecting urban flow. Second, in order to further analyze urban flow, we partitioned the preparation process of multi-source spatiotemporal data related with urban flow into three groups. Third, we chose the spatiotemporal dynamic data as a case study for the urban flow prediction task. Fourth, we analyzed and compared some representative flow prediction methods in detail, classifying them into five categories: statistics-based, traditional machine learning-based, deep learning-based, reinforcement learning-based, and transfer learning-based methods. Finally, we showed open challenges of urban flow prediction and discussed many recent research works on urban flow prediction. This paper will facilitate researchers to find suitable methods and public datasets for addressing urban spatiotemporal flow forecast problems. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
15662535
Volume :
59
Database :
Academic Search Index
Journal :
Information Fusion
Publication Type :
Academic Journal
Accession number :
142578714
Full Text :
https://doi.org/10.1016/j.inffus.2020.01.002