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Determining bus stop locations using deep learning and time filtering
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Metadata
Document Title
Determining bus stop locations using deep learning and time filtering
Author
Piriyataravet J., Kumwilaisak W., Chinrungrueng J., Piriyatharawet T.
Name from Authors Collection
Scopus Author ID
15219541000
Affiliations
Department of Electronics and Telecommunication Engineering, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand; Health Innovation and Information Research Team, Assistive Technology and Medical Devices Research Center, National Science and Technology Development Agency, Pathum Thani, Thailand; School of Computer Science and Engineering, Nanyang Technological University, Singapore
Type
Article
Source Title
Engineering Journal
ISSN
01258281
Year
2021
Volume
25
Issue
8
Page
163-172
Open Access
Bronze, Green
Publisher
Chulalongkorn University, Faculty of Fine and Applied Arts
DOI
10.4186/ej.2021.25.8.163
Abstract
This paper presents an intelligent bus stop determination from bus Global Positioning System (GPS) trajectories. A mixture of deep neural networks and a time filtering algorithm is used in the proposed algorithm. A deep neural network uses the speed histogram and azimuth angle at each location as input features. A deep neural networks consists of the convolutional neural networks (CNN), fully connected networks, and bidirectional Long-Short Term Memory (LSTM) networks. It predicts the soft decisions of bus stops at all locations along the route. The time filtering technique was adopted to refine the results obtained from the LSTM net-work. The time histograms of all locations was built where the high potential timestamps are extracted. Then, a linear regression is used to produce an approximate reliable timestamp. Each time distribution can be derived using data updated at that time slot and compared to a reference distribution. Locations are predicted as bus stop locations when timestamp distributions close to the reference distributions. Our technique was tested on real bus service GPS data from National Science and Technology Development Agency (NATDA, Thailand). The proposed method can outperform other existing bus stop detection systems. © 2021, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.
License
CC BY-NC-ND
Rights
EJ
Publication Source
Scopus