Traffic Analysis of Important Road Junctions Based on Traffic Flow Indicators

  • Hung-Chi ChuEmail author
  • Chi-Kun Wang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 513)


Intelligent transportation systems can be used as an indicator of urban sophistication. Therefore, many countries devote themselves to developing an intelligent transportation system. The construction of intelligent transport system in Taiwan is still in infancy. The allocation of traffic signal lights requires a lot of manpower and time to observe the site. Based on collected information, data integrity was inadequate due to human negligence. The traffic congestion problems caused by the mistake can’t be improved. In this article, the traffic flow based on the intersection traffic flow indicators analysis is proposed. This method is based on the relationship between data to judge, to avoid the problem of empirical judgment error without human intervention. Traffic flow are collected by the vehicle detector to improve data integrity. Using the indicator for road junction association analysis can find the association rules between road junctions. Finally, a deep neural network is used as its classifier, so the accuracy is over 95%.


Intelligent transportation system Association analysis Deep neural network 



This study was supported by the Ministry of Science and Technology (No. MOST 105-2221-E-324-009-MY2) of Taiwan.


  1. 1.
    Anagnostopoulos T, Zaslavsky A, Kolomvatsos K, Medvedev A, Amirian P, Morley J, Hadjieftymiades S (2017) Challenges and opportunities of waste management in IoT-enabled smart cities: a survey. IEEE Trans Sustain Comput 2(3):75–289CrossRefGoogle Scholar
  2. 2.
    Xu X, Wang W, Liu Y, Zhao X, Xu Z, Zhou H (2016) A bibliographic analysis and collaboration patterns of ieee transactions on intelligent transportation systems between 2000 and 2015. IEEE Trans Intell Transp Syst 17(8):2238–2247CrossRefGoogle Scholar
  3. 3.
    Younis O, Moayeri N (2017) Employing cyber-physical systems: dynamic traffic light control at road intersections. IEEE Internet Things J 4(6):2286–2296CrossRefGoogle Scholar
  4. 4.
    Contreras S, Kachroo P, Agarwal S (2015) Observability and sensor placement problem on highway segments: a traffic dynamics-based approach. IEEE Trans Intell Transp Syst 17(3):848–858CrossRefGoogle Scholar
  5. 5.
    Li J, Mei X, Prokhorov D, Tao D (2016) Deep neural network for structural prediction and lane detection in traffic scene. IEEE Trans Neural Netw Learn Syst 28(3):690–703CrossRefGoogle Scholar
  6. 6.
    Martinez C, Velasquez JD (2016) An efficient new scheme of fitness evaluation in genetic programming using the R language. IEEE Latin America Trans 14(4):1866–1869CrossRefGoogle Scholar
  7. 7.
    Witten IH, Frank E, Hall MA, Pal CJ (2016) Data mining: practical machine learning tools and techniques, 4th edn. Morgan Kaufmann, San Mateo, CA, USAGoogle Scholar
  8. 8.
    Chakravorti T, Patnaik RK, Dash PK (2018) Detection and classification of islanding and power quality disturbances in microgrid using hybrid signal processing and data mining techniques. IET Signal Proc 12(1):82–94CrossRefGoogle Scholar
  9. 9.
    Lughofer E, Pratama M (2017) Online active learning in data stream regression using uncertainty sampling based on evolving generalized fuzzy models. IEEE Trans Fuzzy Syst 26(1):292–309CrossRefGoogle Scholar
  10. 10.
    Wang J, Zhang F, Liu F, Ma J (2016) Hybrid forecasting model-based data mining and genetic algorithm-adaptive particle swarm optimization: a case study of wind speed time series. IET Renew Power Gener 10(3):287–298CrossRefGoogle Scholar
  11. 11.
    Wu W, Peng M (2017) A Data mining approach combining k -means clustering with bagging neural network for short-term wind power forecasting. IEEE Internet Things J 4(4):979–986MathSciNetCrossRefGoogle Scholar
  12. 12.
    Sheng G, Hou H, Jiang X, Chen Y (2016) A novel association rule mining method of big data for power transformers state parameters based on probabilistic graph model. IEEE Trans Smart Grid 9(2):695–702CrossRefGoogle Scholar
  13. 13.
    Munk M, Drlík M, Benko L, Reichel J (2017) Quantitative and qualitative evaluation of sequence patterns found by application of different educational data preprocessing techniques. IEEE Access 5:8989–9004CrossRefGoogle Scholar
  14. 14.
    LeCun Y, Bengio Y, Hinton GE (2015) Deep learning. Nature 521:436–444CrossRefGoogle Scholar
  15. 15.
    Ullah A, Ahmad J, Muhammad K, Sajjad M, Baik SW (2017) Action recognition in video sequences using deep bi-directional lstm with cnn features. IEEE Access 6:1155–1166CrossRefGoogle Scholar
  16. 16.
    Gelly G, Gauvain JL (2017) Optimization of rnn-based speech activity detection. IEEE/ACM Trans Audio Speech Language Process 26(3):646–656CrossRefGoogle Scholar
  17. 17.
    Lai YH, Chen F, Wang SS, Lu X, Tsao Y, Lee CH (2016) A deep denoising autoencoder approach to improving the intelligibility of vocoded speech in cochlear implant simulation. IEEE Trans Biomed Eng 64(7):1568–1578CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Chaoyang University of TechnologyWufeng DistrictTaiwan

Personalised recommendations