Trip destination prediction based on a deep integration network by fusing multiple features from taxi trajectories-Reference-Cited by-同舟云学术

Trip destination prediction based on a deep integration network by fusing multiple features from taxi trajectories

Published:2021-06-12 Issue: Volume: Page:
ISSN:1751-956X
Container-title:IET Intelligent Transport Systems
language:en
Short-container-title:IET intell. transp. syst

Author:

Tang Jinjun¹^ORCID,Liang Jian¹,Yu Tianjian¹,Xiong Yong²,Zeng Guoliang²

Affiliation:

1. Smart Transportation Key Laboratory of Hunan Province School of Traffic and Transportation Engineering Central South University Changsha 410075 China

2. Department of Traffic and Transportation Hunan Lianzhi Technology Co., Ltd Changsha 410200 China

Publisher

Institution of Engineering and Technology (IET)

Subject

Law,Mechanical Engineering,General Environmental Science,Transportation

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/itr2.12075

Reference43 articles.

1. Using GPS to learn significant locations and predict movement across multiple users

2. Combining Markov model and Prediction by Partial Matching compression technique for route and destination prediction

3. Estimating hotspots using a Gaussian mixture model from large-scale taxi GPS trace data

4. Inferring High-Level Behavior from Low-Level Sensors

5. Learning and inferring transportation routines

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