Predicting future locations of moving objects with deep fuzzy-LSTM networks-Reference-Cited by-同舟云学术

Predicting future locations of moving objects with deep fuzzy-LSTM networks

Published:2018-12-05 Issue:1 Volume:16 Page:119-136
ISSN:2324-9935
Container-title:Transportmetrica A: Transport Science
language:en
Short-container-title:Transportmetrica A: Transport Science

Author:

Li Mingxiao¹²,Lu Feng¹²³⁴,Zhang Hengcai¹²³^ORCID,Chen Jie¹

Affiliation:

1. State Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing People’s Republic of China

2. University of the Chinese Academy of Sciences, Beijing People’s Republic of China

3. Fujian Collaborative Innovation Center for Big Data Applications in Governments, Fuzhou People’s Republic of China

4. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing People’s Republic of China

Funder

National Natural Science Foundation of China

Key Research Program of the Chinese Academy of Sciences

National Key Research and Development Program

Publisher

Informa UK Limited

Subject

General Engineering,Transportation

Link

https://www.tandfonline.com/doi/pdf/10.1080/23249935.2018.1552334

Reference28 articles.

1. NLPMM: A Next Location Predictor with Markov Modeling

2. Spatiotemporal data model for network time geographic analysis in the era of big data

3. Temporal Pattern Mining of Moving Objects for Location-Based Service

4. Learning to Forget: Continual Prediction with LSTM

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