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Appl. Sci. 2019, 9(4), 615; https://doi.org/10.3390/app9040615

Improved Spatio-Temporal Residual Networks for Bus Traffic Flow Prediction

Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
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Received: 28 December 2018 / Revised: 1 February 2019 / Accepted: 7 February 2019 / Published: 13 February 2019
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Abstract

Buses, as the most commonly used public transport, play a significant role in cities. Predicting bus traffic flow cannot only build an efficient and safe transportation network but also improve the current situation of road traffic congestion, which is very important for urban development. However, bus traffic flow has complex spatial and temporal correlations, as well as specific scenario patterns compared with other modes of transportation, which is one of the biggest challenges when building models to predict bus traffic flow. In this study, we explore bus traffic flow and its specific scenario patterns, then we build improved spatio-temporal residual networks to predict bus traffic flow, which uses fully connected neural networks to capture the bus scenario patterns and improved residual networks to capture the bus traffic flow spatio-temporal correlation. Experiments on Beijing transportation smart card data demonstrate that our method achieves better results than the four baseline methods. View Full-Text
Keywords: spatio-temporal; residual networks; bus traffic flow prediction spatio-temporal; residual networks; bus traffic flow prediction
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Liu, P.; Zhang, Y.; Kong, D.; Yin, B. Improved Spatio-Temporal Residual Networks for Bus Traffic Flow Prediction. Appl. Sci. 2019, 9, 615.

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