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, Volume 22, Issue 5, pp 2129–2151 | Cite as

IQGA: A route selection method based on quantum genetic algorithm- toward urban traffic management under big data environment

  • Yuefei Tian
  • Wenbin HuEmail author
  • Bo DuEmail author
  • Simon Hu
  • Cong Nie
  • Cheng Zhang
Part of the following topical collections:
  1. Special Issue on Big Data Management and Intelligent Analytics


The increasingly serious problem of traffic congestion has become a critical issue that urban managers need to focus on. However, as urban scale and structure have already taken shape, the use of existing road resources to achieve effective route selection for vehicles is the key to solving this traffic congestion problem. Existing research has mainly focused on the following three points: (1) algorithms for controlling traffic signal lamp period at single intersections; (2) route recommendation algorithms for a single vehicle; and (3) route recommendation algorithms based on the traffic history experienced by a vehicle. These studies, however, have the following limitations: (1) the evaluation factor is singular, and therefore, cannot fully express the advantages and disadvantages of the route selection method; (2) real-time route selection is absent; (3) route selection for a single vehicle is ineffective in avoiding local congestion. In view of these problems, this paper proposes an improved quantum genetic algorithm (IQGA) to solve the problem of traffic congestion in route selection. The algorithm includes the following: (1) proposing a quantum chromosome initialization strategy (QCIS) to convert and code real traffic conditions and to construct quantum chromosomes based on the quantum coding for vehicles and roads; (2) proposing a quantum chromosome mapping algorithm (QCMA) to transform the calculation bits of quantum chromosomes into the results of route selection for different vehicles; (3) proposing a contemporary optimal solution decision strategy (COSDS) to judge the current route selection results; (4) proposing a quantum update algorithm (QUA) to update and iterate the quantum coding of the population. Two types of experiments were conducted in this study: (1) Artificial traffic networks with different scales were designed to carry out comparative experiments between IQGA and other algorithms. The experimental results show that IGQA has better robustness and adaptive ability. (2) Comparative experiments on an actual urban traffic network verified the high-performance and real-time performance capabilities of IQGA.


Traffic congestion Route selection Multi-intersection Chromosome mapping Quantum genetic 



This work is partially supported by National Natural Science Foundation of China (61572369, 61711530238). We sincerely thank the reviewers, editors, peers, teachers and students who have given support and advice to the work of this paper.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer SciencesWuhan UniversityWuhanChina
  2. 2.Hubei Bosheng Digital Education Service Co., Ltd.WuhanChina
  3. 3.Civil and Environmental Engineering DepartmentImperial College LondonLondonUK

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