Next Article in Journal
Application of Self-Organizing Neural Networks to Electrical Fault Classification in Induction Motors
Next Article in Special Issue
Optimization of EPB Shield Performance with Adaptive Neuro-Fuzzy Inference System and Genetic Algorithm
Previous Article in Journal
Effective Crack Damage Detection Using Multilayer Sparse Feature Representation and Incremental Extreme Learning Machine
Article Menu
Issue 4 (February-2) cover image

Export Article

Open AccessArticle
Appl. Sci. 2019, 9(4), 615;

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
Author to whom correspondence should be addressed.
Received: 28 December 2018 / Revised: 1 February 2019 / Accepted: 7 February 2019 / Published: 13 February 2019
Full-Text   |   PDF [2886 KB, uploaded 13 February 2019]   |  


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

Figure 1

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).

Share & Cite This Article

MDPI and ACS Style

Liu, P.; Zhang, Y.; Kong, D.; Yin, B. Improved Spatio-Temporal Residual Networks for Bus Traffic Flow Prediction. Appl. Sci. 2019, 9, 615.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top