Advertisement

Using Loop Detector Big Data and Artificial Intelligence to Predict Road Network Congestion

  • Zhi-wei Guan
  • Xi-yao Liu
  • Ling Yang
  • Hong-lin Zhao
  • Xiao-feng Liu
  • Feng Du
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 503)

Abstract

Understanding the temporal-spatial congestion evolution is important to mitigate traffic congestion and improve traffic efficiency. Most studies used floating car data to analyze the urban congestion, however, its market penetrate is low in many cities, thus the data is not enough in terms of quantity and coverage. Loop detector is the most frequently used sensor, its data has the attributes of long-term and large-scale coverage, and utilizing the loop detector big data is helpful to analyze the congestion evolution. Therefore, this study proposes a data-driven congestion analysis approach, which consists of loop detector data processing, traffic simulation, and artificial intelligence to predict the urban temporal-spatial congestion evolution. A case study in Tianjin, China is conducted, and the case study result shows that the evening peak has more serious traffic congestion than the morning peak, the prediction accuracy of feed forward back-propagation neural network (BPNN) increases with the time interval aggregation level increasing, and the prediction accuracy is 85.7% with 30 min interval aggregation.

Keywords

Traffic congestion Traffic big data Artificial intelligence Traffic simulation 

Notes

Acknowledgements

This research was supported by the Service Platform of Intelligent Transportation Cooperative Control Technologies (16PTGCCX00150), the National Natural Science Foundation of China (51408417, 61503284), the Key Project of Natural Science Foundation of Tianjin (16JCZDJC38200), the Transportation Science and Technology Development Plan Project of Tianjin (2017A-24), and the Science and Technology Plan Project of Tianjin (17ZXRGGX00070, 17KPXMSF00010).

References

  1. 1.
    Schrank D, Eisele B, Lomax T (2015) TTI’s 2015 urban mobility report. In: Proceedings of the 2015 annual urban mobility report, Texas A&M Transportation Institute, Texas, USAGoogle Scholar
  2. 2.
    China news net. Road network performance index increased to 9.2 in Beijing City [EB/OL] (2015-09-25). http://news.sina.com.cn/c/2015-09-25/doc-ifxieynu2298438.shtml
  3. 3.
    Amap company (2015) Traffic operation analysis report of Chinese big cities in 2015. Beijing, ChinaGoogle Scholar
  4. 4.
    Niu XY, Liang D, Song XD (2014) Understanding urban spatial structure of Shanghai Central city based on mobile phone data. Urban Planning Forum 6:61–67 (In Chinese)Google Scholar
  5. 5.
    Chen J, Yang DY (2013) Estimating smart card commuters’ origin-destination distribution based on APTS data. J Transp Syst Eng Inf Technol 13(4):47–53 (In Chinese)MathSciNetGoogle Scholar
  6. 6.
    Liu XF, Guan ZW, Song YQ, Chen DS (2014) An optimization model of UAV route planning for road segment surveillance. J Central South Univ 21(6):2501–2510CrossRefGoogle Scholar
  7. 7.
    Qiu TZ, Lu XY, Chow HF (2009) Real-time density estimation on freeways with loop detector and probe data. In: Proceedings of the 12th IFAC symposium on transportation systems, USA, pp 298–303Google Scholar
  8. 8.
    Rempe F, Huber G, Bogenberger K (2016) Spatio-temporal congestion patterns in urban traffic network. Transp Res Procedia 15:513–524CrossRefGoogle Scholar
  9. 9.
    Kong XJ, Xu ZZ, Shen GJ, Wang JZ, Yang QY, Zhang BS (2016) Urban traffic congestion estimation and prediction based on floating car trajectory data. Future Gener Comput Syst 61:97–107CrossRefGoogle Scholar
  10. 10.
    Wen HM, Sun JP, Zhang X (2014) Study on Traffic congestion patterns of large city in China taking Beijing as an example. Procedia-Soc Behav Sci 138:482–491CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Zhi-wei Guan
    • 1
  • Xi-yao Liu
    • 1
  • Ling Yang
    • 2
  • Hong-lin Zhao
    • 1
  • Xiao-feng Liu
    • 1
  • Feng Du
    • 1
  1. 1.School of Automotive and TransportationTianjin University of Technology and EducationTianjinChina
  2. 2.School of Information Technology EngineeringTianjin University of Technology and EducationTianjinChina

Personalised recommendations