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Sensors and Materials, Volume 32, Number 12(4) (2020)
Copyright(C) MYU K.K.
pp. 4441-4447
S&M2420 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2020.3111
Published: December 29, 2020

Bus Travel Speed Prediction Using Long Short-term Memory Neural Network [PDF]

Seung-Bae Jeon, Myeong-Hun Jeong, Tae-Young Lee, Jeong-Hwan Lee, and Jae-Myoung Cho

(Received September 22, 2020; Accepted December 1, 2020)

Keywords: long short-term memory neural network, bus travel speed prediction, digital tachograph, autoregressive integrated moving average

Improving the accuracy of public transport information has attracted attention in the development of smart cities. We aim to predict the bus travel speed on road sections using a long short-term memory (LSTM) neural network. We use digital tachograph (DTG) data combined with road link data. Motion sensors in DTG can record vehicle’s operation information, such as journey distance, speed, and driving time. The experimental results show that the proposed model based on LSTM performs better than the autoregressive integrated moving average (ARIMA) model. The accuracy was improved by 20% on average.

Corresponding author: Myeong-Hun Jeong


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Cite this article
Seung-Bae Jeon, Myeong-Hun Jeong, Tae-Young Lee, Jeong-Hwan Lee, and Jae-Myoung Cho, Bus Travel Speed Prediction Using Long Short-term Memory Neural Network, Sens. Mater., Vol. 32, No. 12, 2020, p. 4441-4447.

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