Journal of Central South University

, Volume 19, Issue 12, pp 3614–3621 | Cite as

Multi-objective evolutionary approach for UAV cruise route planning to collect traffic information

  • Xiao-feng Liu (刘晓锋)Email author
  • Zhong-ren Peng (彭仲仁)
  • Yun-tao Chang (常云涛)
  • Li-ye Zhang (张立业)


Unmanned aerial vehicle (UAV) was introduced as a novel traffic device to collect road traffic information and its cruise route planning problem was considered. Firstly, a multi-objective optimization model was proposed aiming at minimizing the total cruise distance and the number of UAVs used, which used UAV maximum cruise distance, the number of UAVs available and time window of each monitored target as constraints. Then, a novel multi-objective evolutionary algorithm was proposed. Next, a case study with three time window scenarios was implemented. The results show that both the total cruise distance and the number of UAVs used continue to increase with the time window constraint becoming narrower. Compared with the initial optimal solutions, the optimal total cruise distance and the number of UAVs used fall by an average of 30.93% and 31.74%, respectively. Finally, some concerns using UAV to collect road traffic information were discussed.

Key words

traffic information collection unmanned aerial vehicle cruise route planning multi-objective optimization 


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  1. [1]
    KENZO N. Prospect and recent research and development for civil use autonomous unmanned aircraft as UAV and MAV [J]. Journal of System Design and Dynamics, 2007, 1(2):120–128.CrossRefGoogle Scholar
  2. [2]
    HUTCHISON M G. A method for estimating range requirements of tactical reconnaissance UAVs [C]// Proceedings of AIAA’s 1st Technical Conference and Workshop on Unmanned Aerospace Vehicles. Virginia: AIAA, 2002: 1-12.Google Scholar
  3. [3]
    TIAN Jing, SHEN Lin-cheng, ZHENG Yan-xing. Genetic algorithm based approach for multi-UAV cooperative reconnaissance mission planning problem [C]// International Symposium on Methodologies for Intelligent Systems. Berlin: Springer, 2006: 101–110.Google Scholar
  4. [4]
    YAN Qin-yu, PENG Zhong-ren, CHANG Yun-tao. Unmanned aerial vehicle cruise route optimization model for sparse road network [C]//Transportation Research Board of the National Academies, Washington D C: National Research Council, 2011: 432–445.Google Scholar
  5. [5]
    WANG Zhen-hua, ZHANG Wei-guo, SHI Jing-ping, HAN Yin. UAV route planning using multiobjective ant colony system [C]// IEEE Conference on Cybernetics and Intelligent Systems, Chengdu: IEEE, 2008: 797–800.CrossRefGoogle Scholar
  6. [6]
    LIU Xiao-feng, PENG Zhong-ren, ZHANG Li-ye, LI Li. Unmanned aerial vehicle route planning for traffic information collection [J]. Journal of Transportation Systems Engineering and Information Technology, 2012, 12(1): 61–66.Google Scholar
  7. [7]
    SRINIVAS N, DEB K. Multi-objective optimization using nondominated sorting in genetic algorithms [J]. Evolutionary Computation, 1994, 2(3): 221–248.CrossRefGoogle Scholar
  8. [8]
    FONSECA C M, FLEMING P J. Genetic algorithms for multiobjective optimization: formulation, discussion and generalization [C]// Proceedings of the Fifth International Conference on Genetic Algorithms. San Mateo: Morgan Kaufmann Publishers, 1993: 416–423.Google Scholar
  9. [9]
    ZITZLER E, THIELE L. Multi-objective evolutionary algorithms: A comparative case study and the strength Pareto approach [J]. IEEE Transactions on Evolutionary Computation, 1999, 3(4): 257–271.CrossRefGoogle Scholar
  10. [10]
    HORN J, NAFPLIOTIS N, GOLDBERG D E. A niched pareto genetic algorithm for multiobjective optimization [C]// Proceedings of the First IEEE Conference on Evolutionary Computation, Piscataway: IEEE, 1994: 82–87.Google Scholar
  11. [11]
    KNOWLES J, CORNE D. The pareto archived evolution strategy: A new baseline algorithm for pareto multiobjective optimization [C] // Proceedings of the 1999 Congress on Evolutionary Computation, Washington D C: IEEE, 1999: 98–105.Google Scholar
  12. [12]
    DEB K, PRATAP A, AGARWAL S, DEB K, PRATAP A, AGARWAL S, MEYARIVAN T. A fast and elitist multi-objective genetic algorithm: NSGA-II [J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182–197.CrossRefGoogle Scholar
  13. [13]
    BOYLE D P, GUPTA H V, SOEOOSHIAN S. Toward improved calibration of hydrological models: Combining the strengths of manual and automatic methods [J]. Water Resources Research, 2000, 36(12): 3663–3674.CrossRefGoogle Scholar
  14. [14]
    DHANALAKSHMI S, KANNAN S, MAHADEVAN K. Application of modified NSGA-II algorithm to combined economic and emission dispatch problem [J]. International Journal of Electrical Power & Energy Systems, 2011, 33(4): 992–1002.CrossRefGoogle Scholar
  15. [15]
    LANG Mao-xiang. Distribution vehicle scheduling optimization model and its algorithm [M]. Beijing: Electronic Industry Press, 2009. (in Chinese)Google Scholar

Copyright information

© Central South University Press and Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Xiao-feng Liu (刘晓锋)
    • 1
    Email author
  • Zhong-ren Peng (彭仲仁)
    • 1
  • Yun-tao Chang (常云涛)
    • 1
  • Li-ye Zhang (张立业)
    • 1
  1. 1.School of Transportation EngineeringTongji UniversityShanghaiChina

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