Traffic Flow Correlation Analysis of K Intersections Based on Deep Learning

  • Hung-Chi ChuEmail author
  • Chi-Kun Wang
  • Yi-Xiang Liao
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 110)


An Intelligent transportation system is one of the indispensable systems of smart cities. The most important goal of an intelligent transportation system is to effectively reduce traffic congestion. This paper presents an analysis of traffic congestion based on traffic flows. According to the result of this analysis, the intersection correlation in a specific area can be deduced. This analysis method more effectively than the traditional method finds the relationship between the intersections according to traffic information, so through deep neural network classify intersection congestion levels, the accuracy rate is higher than 96.7%.


Intelligent transportation system Deep neural network 



This research was supported in part by the Ministry of Science and Technology, Taiwan, ROC, under grant MOST 105-2221-E-324-009-MY2 and MOST 107-2221-E-324-003-MY2.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Chaoyang University of TechnologyTaichungTaiwan

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