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Title: Determining bus stop locations using deep learning and time filtering
Authors: Piriyataravet, Jitpinun
Kumwilaisak, Wuttipong
Chinrungrueng, Jatuporn
Piriyatharawet, Teerawat
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: Piriyataravet, J., Kumwilaisak, W., Chinrungrueng, J. & Piriyatharawet, T. (2021). Determining bus stop locations using deep learning and time filtering. Engineering Journal, 25(8), 163-172.
Journal: Engineering Journal 
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.
ISSN: 0125-8281
DOI: 10.4186/ej.2021.25.8.163
Schools: School of Computer Science and Engineering 
Rights: © 2021 Faculty of Engineering, Chulalongkorn University, Phayathai Road, Pathumwan, Bangkok 10330, Thailand. This is an open-access article distributed under the terms of the Creative Commons Attribution License.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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