Skip to main content
SpringerLink
Log in

Application of Wireless Sensor Network in Urban Intelligent Traffic Information Acquisition

  • Published:
Automatic Control and Computer Sciences Aims and scope Submit manuscript

Abstract

With the rapid development of economic and scientific levels, urban traffic in China has been further improved. Moreover, the number of private cars is increasing because of the improvement of living standard. However, various traffic problems such as traffic jam, traffic accidents, disordered traffic order and unreasonable travel structure come along. Therefore, the utilization and development of urban intelligent traffic is an inevitable choice for the improvement of Chinese traffic. This study mainly investigated the application of wireless sensor network in urban intelligent traffic information acquisition. Differential Time of Arrival (DTOA) technology, least square method and Kalman filter algorithm were used to improve the location accuracy of vehicles. Moreover, Matlab simulation experiment was performed. The method proposed in this study can improve the acquisition speed of information and positioning accuracy of vehicles. This work is beneficial to the solution of traffic disorder.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.

Similar content being viewed by others

REFERENCES

  1. Yanxiao, L., Haoshan, S., and Shuiping, Z., An energy-efficient MAC protocol for wireless sensor network, in Future Wireless Networks and Information Systems, Springer Berlin Heidelberg, 2012.

    Google Scholar 

  2. Tirachini, A., Hensher, D.A., and Rose, J.M., Multimodal pricing and optimal design of urban public transport: The interplay between traffic congestion and bus crowding, Transp. Res. Part B: Methodol., 2014, vol. 61, no. 2, pp. 33–54.

    Article  Google Scholar 

  3. Weiyi, Z., Guan, W., Ailing, H., and Junfang, T., SUE model based on a reliability-admissible reasonable route set, Fifth International Joint Conference on Computational Sciences and Optimization. IEEE, 2012, pp. 458–461.

  4. Ying, Y., Catharin, E.F., and Jeffrey, F.C., Detecting depression severity from vocal prosody, IEEE Trans. Affective Comput., 2013, vol. 4, no. 2, pp. 142–150.

    Article  Google Scholar 

  5. Zhai, Y. and Yeary, M., A new particle filter tracking algorithm for DOA sensor systems, IEEE Instrumentation & Measurement Technology Conference IMTC. IEEE, 2007, pp. 1–4.

    Google Scholar 

  6. Tailin, C., Parameswaran, R., and Kewal, K.S., WSN19-3: Optimal sensor distribution for maximum exposure in a region with obstacles, Global Telecommunications Conference, 2006. GLOBECOM '06, San Francisco, CA, USA, November 27 –December 1: DBLP, 2006, pp. 1–5.

  7. Bejar, R., Krishnamachari, B., Gomes, C., and Selman, B., Distributed constraint satisfaction in a wireless sensor tracking system, Workshop on Distributed Constraint Reasoning, IJCAI-01, 2001.

  8. Tenqchen, S., Yinghaw, S., Mingchang, S., and Wushiung, F., Blind signal extraction algorithm for the license plate matching of vehicle positioning system, IEEE International Conference on Field-Programmable Technology, 2004, pp. 440–442.

  9. Ziyi, Y., An unequal clustering strategy for WSNs based urban intelligent transportation system, J. Inf. Comput. Sci., 2015, vol. 12, no. 10, pp. 4001–4012.

    Article  Google Scholar 

  10. Jirui, L. and Kai, Y., Improvement of path planning in mobile beacon assisted positioning, Advanced Intelligent Computing Theories and Applications with Aspects of Artificial Intelligence, International Conference, ICIC, Zhengzhou, China, August 11–14, 2011, Revised Selected Papers. DBLP, 2011, pp. 309–316.

  11. Zijuan, C. and Qingzhong, K., A vehicle management system of community based on radio frequency identification technology, IEEE International Conference on Communication Software and Networks, 2011, pp. 341–343.

  12. Dongqing, W., Feng, D., and Yanyun, C., Data filtering based recursive least squares algorithm for Hammerstein systems using the key-term separation principle, Inf. Sci., 2013, vol. 222, no. 3, pp. 203–212.

    Article  MathSciNet  MATH  Google Scholar 

  13. Feng, S. and Lijun, T., Cubature Kalman filter—Kalman filter algorithm, Control Decis., 2012, vol. 27, no. 10, pp. 1561–1565.

    MathSciNet  Google Scholar 

  14. Nan, H. and Shengchuan, Z., Discussion of basic elasticity models applied to induced traffic in China, J. East. Asia Soc. Transp. Stud., 2013, vol. 10, pp. 1687–1700.

    Google Scholar 

  15. Jianping, Z., Baotong, W., and Haiqing, X., Improved Kalman filter algorithm in positioning system for autonomous mobile robot, Appl. Res. Comput., 2011, no. 5, pp. 1710–1712.

  16. Bokui, C., Yanbo, X., Wei, T., Chuanfei, D., Dongmei, S., and Binghong, W., A comprehensive study of advanced information feedbacks in real-time intelligent traffic systems, Phys. A Stat. Mech. Its Appl., 2012, vol. 391, no. 8, pp. 2730–2739.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Beijing Polytechnic, 100176, Beijing, China

    Niqin Jing

Authors
  1. Niqin Jing
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Niqin Jing.

Additional information

The article is published in the original.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Niqin Jing Application of Wireless Sensor Network in Urban Intelligent Traffic Information Acquisition. Aut. Control Comp. Sci. 52, 431–438 (2018). https://doi.org/10.3103/S0146411618050103

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S0146411618050103

Keywords:

Search

Navigation