Determining bus stop locations using deep learning and time filtering
Journal article
Authors/Editors
Strategic Research Themes
Publication Details
Author list: Piriyataravet J., Kumwilaisak W., Chinrungrueng J., Piriyatharawet T.
Publisher: Faculty of Engineering
Publication year: 2021
Volume number: 25
Issue number: 8
Start page: 163
End page: 172
Number of pages: 10
ISSN: 01258281
eISSN: 0125-8281
Languages: English-Great Britain (EN-GB)
View in Web of Science | View on publisher site | View citing articles in Web of Science
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.
Keywords
Bidirectional LSTM, Bus stop determination, Deep Learning, Global Positioning System
Contact Information