Skip to main content

Advertisement

SpringerLink
Log in

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

  • Published:
World Wide Web Aims and scope Submit manuscript

Abstract

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.

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

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6

Similar content being viewed by others

References

  1. Arokhlo M Z, Selamat A, Hashim S Z M, et al. Route guidance system using multi-agent reinforcement learning[C], International Conference on Information Technology in Asia. 2011, 1–5

  2. Bezuglov, A., Comert, G.: Short-term freeway traffic parameter prediction: Application of grey system theory models. Expert Systems with Applications, Volume. 62(15), 284–292 (November 2016)

    Article  Google Scholar 

  3. Kit Yan Chan , Tharam Dillon , Elizabeth Chang , Jaipal Singh. Prediction of Short-Term Traffic Variables Using Intelligent Swarm-Based Neural Networks, IEEE Transactions on Control Systems Technology , Vol (21), no. 1, 2012, 263–274

  4. Desai, P., Loke, S.W., Desai, A., et al.: CARAVAN: Congestion Avoidance and Route Allocation Using Virtual Agent Negotiation. IEEE Trans. Intell. Transp. Syst. 14(3), 1197–1207 (2013)

    Article  Google Scholar 

  5. Fernando, O., Nicolas, E.S.: Wardrop Equilibria with Risk-Averse Users [J]. Transp. Sci. 44(1), 63–86 (2010)

    Article  Google Scholar 

  6. Grivokostopoulou Foteini, Hatzilygeroudis Loannis. An automatic marking system for interactive exercises on blind search algorithms. Artificial Intelligence in Education - 16th International Conference, AIED: Memphis. 2013, 783–786 (2013)

    Google Scholar 

  7. Groot N, De Schutter B, Hellendoorn H. Toward System-Optimal Routing in Traffic Networks: A Reverse Stackelberg Game Approach [J]. IEEE Trans. Intell. Transp. Syst., 2015, 16(1):29–40

  8. He Zhonghe, Wang Li, Li Dai, Zhang Lingyu. Steady-State Signal Control for Urban Traffic Networks, 2015 IEEE 18th International Conference on Intelligent Transportation Systems, 15–18 Sept. 2015, 463–470

  9. Jinghao, S., Lan, G., Deng, Q., Xin, Z., Yang, F.: Modeling Urban Traffic Control Systems from the Perspective of Real Time Calculus. Journal of Software. 27(3), 527–546 (2016)

    MathSciNet  Google Scholar 

  10. Kim Jisoo, Park Bumjin, Moon Byung-Sup, Eo Hyo-Kyoung. Improvement of bus arrival time estimation model by weighted moving average method. 21st World Congress on Intelligent Transport Systems, ITSWC: Detroit, September. 2014 (2014)

  11. Kouvelas, A., Aboudolas, K., Papageorgiou, M., Kosmatopoulos, E.B.: A Hybrid Strategy for Real-Time Traffic Signal Control of Urban Road Networks, pp. 884–894. IEEE Transactions on Intelligent Transportation Systems, Sept (2011)

    Google Scholar 

  12. Lee Jeng-Wei, Lo Chun-Chih, Tang Shih-Pu, Horng Mong-Fong, Kuo Yau-Hwang. A hybrid traffic geographic routing with cooperative traffic information collection scheme in VANET, Advanced Communication Technology (ICACT), 2011 13th International Conference on, 13–16 Feb. 2011, 1496–1501

  13. Lipets, V., Vanetik, N., Gudes, E.: Subsea: an efficient heuristic algorithm for subgraph isomorphism. Data Min. Knowl. Disc. 19(3), 320–350 (December 2009)

    Article  MathSciNet  Google Scholar 

  14. Liping, Y., Hu, W., Wang, H., Zhenyu, Q., Bo, D.: Dynamic Real-Time Algorithm for Multi-Intersection Route Selection in Urban Traffic Networks. Journal of Software. 27(9), 2199–2217 (2016)

    MathSciNet  Google Scholar 

  15. Liu, K., Yang, B., Shang, S., Li, Y., & Ding, Z. (2013). MOIR/UOTS: Trip Recommendation with User Oriented Trajectory Search. IEEE, International Conference on Mobile Data Management (Vol. 1, pp. 335-337). IEEE

  16. Mingjun, W.: Meng Yu. Research on the optimal route choice based on improved Dijkstra. Advanced Research and Technology in Industry Applications (WARTIA). IEEE Workshop, Ottawa. 2014, 303–306 (2014)

    Google Scholar 

  17. Shinsuke Nakajima, Daisuke Kitayama, Yoshitaka Sushita, Kazutoshi Sumiya, Naiwala P. Chandrasiri, Kazunari Nawa, Route recommendation method for car navigation system based on estimation of driver's intent, Vehicular Electronics and Safety (ICVES), 2012 IEEE International Conference on, 24–27 July 2012, 318–323

  18. Negulescu S C, Kifor C V, Oprean C. Ant Colony Solving Multiple Constraints Problem: Vehicle Route Allocation[J]. International Journal of Computers Communications & Control, 2008, iii (4), 366–373

  19. Ordóñez, F., Stier-Moses, N.E.: Wardrop equilibria with risk-averse users[J]. Transp. Sci. 44(1), 63–86 (2010)

    Article  Google Scholar 

  20. Pan, J., Popa, I.S., Zeitouni, K., et al.: Proactive Vehicular Traffic Rerouting for Lower Travel Time. IEEE Trans. Veh. Technol. 62(8), 3551–3568 (2013)

    Article  Google Scholar 

  21. Pandit, K., Dipak, G., Michael Zhang, H., Chuah, C.-N.: Adaptive Traffic Signal Control with Vehicular Ad hoc Networks, pp. 1459–1471. IEEE Transactions on Vehicular Technology, May (2013)

    Google Scholar 

  22. Tomas Potuzak. Feasibility study of optimization of a genetic algorithm for traffic network division for distributed road traffic simulation, 2013 6th International Conference on Human System Interactions (HSI), 6–8 June 2013, 372–379

  23. Mahmood Rahmani, Erik Jenelius, Haris N. Koutsopoulos. Non-parametric estimation of route travel time distributions from low-frequency floating car data. Transportation Research Part C: Emerging Technologies, Volume 58, Part B, September 2015, 343–362

  24. Rhee Kyoung-Ah, Kim Joon-Ki, Lee Young-ihn, Ulfarsson Gudmundur F: Spatial regression analysis of traffic crashes in Seoul. Accid. Anal. Prev. 91, 190–199 (June 2016)

    Article  Google Scholar 

  25. Sedeño-Noda, A., Raith, A.: A Dijkstra-like method computing all extreme supported non-dominated solutions of the biobjective shortest path problem. Comput. Oper. Res. 57, 83–94 (May 2015)

    Article  MathSciNet  MATH  Google Scholar 

  26. Shang, S., Ding, R., Yuan, B., Xie, K., Zheng, K., & Kalnis, P. (2012). User oriented trajectory search for trip recommendation. EDBT (pp. 156-167)

  27. Shang, S., Ding, R., Zheng, K., Jensen, C.S., Kalnis, P., Zhou, X.: Personalized trajectory matching in spatial networks. VLDB J. 23(3), 449–468 (2014)

    Article  Google Scholar 

  28. Shang, S., Zheng, K., Jensen, C.S., Yang, B., Kalnis, P., Li, G., et al.: Discovery of path nearby clusters in spatial networks. IEEE Transactions on Knowledge & Data Engineering. 27(6), 1505–1518 (2015)

    Article  Google Scholar 

  29. Shang, S., Liu, J., Zheng, K., Lu, H., Pedersen, T.B., Wen, J.R.: Planning unobstructed paths in traffic-aware spatial networks. Geoinformatica. 19(4), 723–746 (2015)

    Article  Google Scholar 

  30. Shang, S., Chen, L., Wei, Z., Jensen, C.S., Wen, J.R., Kalnis, P.: Collective travel planning in spatial networks. IEEE Transactions on Knowledge & Data Engineering. 28(5), 1132–1146 (2016)

    Article  Google Scholar 

  31. Shang, S., Chen, L., Wei, Z., Jensen, C.S., Zheng, K., Kalnis, P.: Trajectory similarity join in spatial networks. Proceedings of the Vldb Endowment. 10(11), 1178–1189 (2017)

    Article  Google Scholar 

  32. Shang, S., Chen, L., Jensen, C.S., Wen, J.R., Kalnis, P.: Searching trajectories by regions of interest. IEEE Transactions on Knowledge & Data Engineering. PP(99). 1549–1562 (2017)

  33. Shang, S., Chen, L., Wei, Z., Jensen, C. S., Zheng, K., & Kalnis, P. (2018). Parallel trajectory similarity joins in spatial networks

    Book  Google Scholar 

  34. Sun, R., Ochieng, W.Y., Feng, S.: An integrated solution for lane level irregular driving detection on highways. Transportation Research Part c-emerging Technologies. 56, 61–79 (2015)

    Article  Google Scholar 

  35. Tuberquia-David, M., Vela-Vargas, F., López-Chávez, H., Hernández, C.: A multifractal wavelet model for the generation of long-range dependency traffic traces with adjustable parameters. Expert Systems with Applications, Volume. 62(15), 373–384 (November 2016)

    Article  Google Scholar 

  36. Wang Haizhong, Liu Lu, Qian Zhen, Wei Heng, Dong Shangjia. Empirical mode decomposition-autoregressive integrated moving average: Hybrid short-term traffic speed prediction model. Transportation Research Record, v 2460, n 1, 66–76, December 1, 2014

  37. Wang, S., Djahel, S., Mcmanis, J.: A Multi-Agent based vehicles re-routing system for unexpected traffic congestion avoidance, Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on. IEEE. 2541–2548 (2014)

  38. Wu, W., Yu, T., Tongdan, J.: A label based ant colony algorithm for heterogeneous vehicle routing with mixed backhaul. Applied Soft Computing. 47, 224–234 (October 2016)

    Article  Google Scholar 

  39. Li Xiangling, Tao Xiaofeng, Liu Yinjun, Cui Qimei. Autoregressive model based data gathering algorithm for wireless sensor networks with compressive sensing. IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC, v 2015-December, Hong Kong, 2044-2048

  40. Xie, K., Deng, K., Shang, S., Zhou, X., Zheng, K.: Finding alternative shortest paths in spatial networks. ACM Trans. Database Syst. 37(4), 1–31 (2012)

    Article  Google Scholar 

  41. Xie, K., Deng, K., Shang, S., Zhou, X., Zheng, K.: Finding alternative shortest paths in spatial networks. ACM Trans. Database Syst. 37(4), 1–31 (2012)

    Article  Google Scholar 

  42. Xu, Y., Yuling, Z., Tingting, S., Yanfang, S.: Agent-Based Decentralized Cooperative Traffic Control Toward Green-Waved Effects. Journal of Software. 23(11), 2937–2945 (2012)

    Article  Google Scholar 

  43. Yamada, K., Ma, J., Fukuda, D.: Simulation Analysis of the Market Diffusion Effects of Risk-averse Route Guidance on Network Traffic. Procedia Computer Science. 19, 874–881 (2013)

    Article  Google Scholar 

  44. Yang Jing. Routing method of quantum genetic algorithm. Computer Modelling and New Technologies, 2014, v18, n11, 178–181

  45. Yang, B., Guo, C., Jensen, C. S., Kaul, M., & Shang, S. (2014). Stochastic skyline route planning under time-varying uncertainty. IEEE, International Conference on Data Engineering (Vol. 8, pp. 136-147). IEEE Computer Society

  46. Yufei, Y., Duret, A., van Lint, H.: Mesoscopic Traffic State Estimation based on a Variational Formulation of the LWR Model in Lagrangian-space Coordinates and Kalman Filter. Transportation Research Procedia. 10, 82–92 (2015)

    Article  Google Scholar 

  47. Zheng, K., Shang, S., Yuan, N. J., & Yang, Y. (2013). Towards efficient search for activity trajectories. IEEE, International Conference on Data Engineering (pp. 230-241). IEEE

  48. Zheng, K., Su, H., Zheng, B., Shang, S., Xu, J., & Liu, J., et al. (2015). Interactive Top-k Spatial Keyword queries. IEEE, International Conference on Data Engineering (Vol. 2015-may, pp. 423-434). IEEE

  49. Zhu, S., Zhao, G., Zhao, G., Zhao, G., Wang, J.: Probabilistic routing using multimodal data. Neurocomputing. 253(C), 49–55 (2017)

    Article  Google Scholar 

Download references

Acknowledgements

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.

Author information

Authors and Affiliations

  1. School of Computer Sciences, Wuhan University, Wuhan, Hubei Province, China

    Yuefei Tian, Wenbin Hu, Bo Du, Cong Nie & Cheng Zhang

  2. Hubei Bosheng Digital Education Service Co., Ltd., Wuhan, China

    Yuefei Tian

  3. Civil and Environmental Engineering Department, Imperial College London, London, SW7 2BU, UK

    Simon Hu

Authors
  1. Yuefei Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wenbin Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bo Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Simon Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Cong Nie
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Cheng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Wenbin Hu or Bo Du.

Additional information

Guest Editors: Junping Du, Panos Kalnis, Wenling Li, Shuo Shang

This article belongs to the Topical Collection: Big Data Management and Intelligent Analytics

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tian, Y., Hu, W., Du, B. et al. IQGA: A route selection method based on quantum genetic algorithm- toward urban traffic management under big data environment. World Wide Web 22, 2129–2151 (2019). https://doi.org/10.1007/s11280-018-0594-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11280-018-0594-x

Keywords

Search

Navigation