Abstract
Bus vehicle scheduling is very vital for bus companies to reduce operation cost and guarantee quality of service. Urban roads are easily blocked due to bad weather, such that it is significant to study the bus vehicle scheduling problem under traffic congestion caused by bad weather. In this paper, a dynamic bus vehicle scheduling approach is proposed, which consists of two parts: (1) generate a set of candidate vehicle blocks once the road is blocked; (2) adopt the non-dominated sorting genetic algorithm combined with a departure time adjusting process to select a subset of vehicle blocks from the candidate blocks set to form a vehicle scheduling scheme. Experiments show that our approach can significantly improve quality of service compared to the manual vehicle scheduling scheme.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Sun, L., Lin, L., Li, H., Gen, M.: Hybrid cooperative co-evolution algorithm for uncertain vehicle scheduling. IEEE Access (Early Access) PP, 1 (2018)
Petit, A., Ouyang, Y., Lei, C.: Dynamic bus substitution strategy for bunching intervention. Transp. Res. Part B: Methodol. 115, 1–16 (2018)
Zhu, W., Li, R.: Research on dynamic timetables of bus scheduling based on dynamic programming. In: Proceedings of the 33rd Chinese Control Conference, pp. 8930–8934. IEEE, Nanjing (2014)
Zuo, X., Chen, C., Tan, W., Zhou, M.: Vehicle scheduling of an urban bus line via an improved multiobjective genetic algorithm. IEEE Trans. Intell. Transp. Syst. 16(2), 1030–1041 (2015)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Goldberg, D.: Genetic Algorithm in Search. Optimization and Machine Learning. Addison-Wesley, Boston (1989)
Lin, Y., Pan, S., Jia, L., Zuo, N.: A bi-level multi-objective programming model for bus crew and vehicle scheduling. In: World Congress on Intelligent Control and Automation, pp. 2328–2333. IEEE, Jinan (2010)
Yin, P., Chuang, Y., Lyu, S., Chen, C.: Collaborative vehicle routing and scheduling with cross-docks under uncertainty. In: 2015 IEEE Conference on Collaboration and Internet Computing, pp. 106–112. IEEE, Hangzhou (2015)
Tan, D., Wang, J., Liu, H., Wang, X.: The optimization of bus scheduling based on genetic algorithm. In: International Conference on Transportation, Mechanical, and Electrical Engineering, pp. 1530–1533. IEEE, Changchun (2011)
Li, J., Hu, J., Zhang, Y.: Optimal combinations and variable departure intervals for micro bus system. Tsinghua Sci. Technol. 22(3), 282–292 (2017)
Kwan, R., Wren, A., Kwan, A.: Hybrid genetic algorithms for scheduling bus and train drivers. In: International Congress on Evolutionary Computation, pp. 285–292. IEEE, La Jolla (2000)
Baghoussi, Y., Mendes-Moreira, J., Emmerich, M.: Updating a robust optimization model for improving bus schedules. In: International Conference on Communication Systems and Networks, pp. 619–624. IEEE, Bengaluru (2018)
Song, Y., Ma, J., Guan, W., Liu, T., Chen S.: A multi-objective model for regional bus timetable based on NSGA-II. In: 2012 IEEE International Conference on Computer Science and Automation Engineering, pp. 185–188. IEEE, Zhangjiajie (2012)
Lin, K., Hashimoto, M., Li, Y.: Near-future traffic evaluation based navigation for automated driving vehicles considering traffic uncertainties. In: 2018 19th International Symposium on Quality Electronic Design, pp. 425–431. IEEE, Santa Clara (2018)
Acknowledgment
This work was supported by National Natural Science Foundation of China under Grant 61873040, 61374204, and 61375066.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Shi, H., Wang, C., Zuo, X., Zhao, X. (2018). A Multiobjective Genetic Algorithm Based Dynamic Bus Vehicle Scheduling Approach. In: Qiao, J., et al. Bio-inspired Computing: Theories and Applications. BIC-TA 2018. Communications in Computer and Information Science, vol 952. Springer, Singapore. https://doi.org/10.1007/978-981-13-2829-9_15
Download citation
DOI: https://doi.org/10.1007/978-981-13-2829-9_15
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-2828-2
Online ISBN: 978-981-13-2829-9
eBook Packages: Computer ScienceComputer Science (R0)