Abstract
Bus transit systems are the backbone of public transportation in the United States. An important indicator of the quality of service in such infrastructures is on-time performance at stops, with published transit schedules playing an integral role governing the level of success of the service. However there are relatively few optimization architectures leveraging stochastic search that focus on optimizing bus timetables with the objective of maximizing probability of bus arrivals at timepoints with delays within desired on-time ranges. In addition to this, there is a lack of substantial research considering monthly and seasonal variations of delay patterns integrated with such optimization strategies. To address these, this paper makes the following contributions to the corpus of studies on transit on-time performance optimization: (a) an unsupervised clustering mechanism is presented which groups months with similar seasonal delay patterns, (b) the problem is formulated as a single-objective optimization task and a greedy algorithm, a genetic algorithm (GA) as well as a particle swarm optimization (PSO) algorithm are employed to solve it, (c) a detailed discussion on empirical results comparing the algorithms are provided and sensitivity analysis on hyper-parameters of the heuristics are presented along with execution times, which will help practitioners looking at similar problems. The analyses conducted are insightful in the local context of improving public transit scheduling in the Nashville metro region as well as informative from a global perspective as an elaborate case study which builds upon the growing corpus of empirical studies using nature-inspired approaches to transit schedule optimization.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Arhin, S.A., Noel, E.C., Dairo, O.: Bus stop on-time arrival performance and criteria in a dense urban area. Int. J. Traffic Transp. Eng. 3(6), 233–238 (2014)
American Public Transportation Association: Ridership report archives (2017)
Banks, A., Vincent, J., Anyakoha, C.: A review of particle swarm optimization. Part ii: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications. Natural Comput. 7(1), 109–124 (2008). https://doi.org/10.1007/s11047-007-9050-z
Benn, H.: Bus route evaluation standards, transit cooperative research program, synthesis of transit practice 10. Transportation Research Board, Washington, DC (1995)
Bouyer, A., Hatamlou, A.: An efficient hybrid clustering method based on improved cuckoo optimization and modified particle swarm optimization algorithms. Appl. Soft Comput. 67, 172–182 (2018). https://doi.org/10.1016/j.asoc.2018.03.011. http://www.sciencedirect.com/science/article/pii/S1568494618301273
Chakroborty, P.: Genetic algorithms for optimal urban transit network design. Comput.-Aided Civ. Infrastr. Eng. 18(3), 184–200 (2003)
Chakroborty, P., Deb, K., Subrahmanyam, P.: Optimal scheduling of urban transit systems using genetic algorithms. J. Transp. Eng. 121(6), 544–553 (1995)
Dhabal, S., Sengupta, S.: Efficient design of high pass fir filter using quantum-behaved particle swarm optimization with weighted mean best position. In: Proceedings of the 2015 Third International Conference on Computer, Communication, Control and Information Technology (C3IT), pp. 1–6, February 2015. https://doi.org/10.1109/C3IT.2015.7060145
Eranki, A.: A model to create bus timetables to attain maximum synchronization considering waiting times at transfer stops (2004)
Fan, W., Machemehl, R.B.: Optimal transit route network design problem with variable transit demand: genetic algorithm approach. J. Transp. Eng. 132(1), 40–51 (2006)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning, 1st edn. Addison-Wesley Longman Publishing Co., Inc., Boston (1989)
Hairong, Y., Dayong, L.: Optimal regional bus timetables using improved genetic algorithm. In: Second International Conference on Intelligent Computation Technology and Automation, ICICTA 2009, vol. 3, pp. 213–216. IEEE (2009)
Ibarra-Rojas, O.J., Rios-Solis, Y.A.: Synchronization of bus timetabling. Transp. Res. Part B: Methodol. 46(5), 599–614 (2012)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948, November 1995. https://doi.org/10.1109/ICNN.1995.488968
Khiari, J., Moreira-Matias, L., Cerqueira, V., Cats, O.: Automated setting of bus schedule coverage using unsupervised machine learning. In: Bailey, J., Khan, L., Washio, T., Dobbie, G., Huang, J.Z., Wang, R. (eds.) PAKDD 2016. LNCS (LNAI), vol. 9651, pp. 552–564. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-31753-3_44
Kodinariya, T.M., Makwana, P.R.: Review on determining number of cluster in k-means clustering. Int. J. 1(6), 90–95 (2013)
Nayeem, M.A., Rahman, M.K., Rahman, M.S.: Transit network design by genetic algorithm with elitism. Transp. Res. Part C: Emerg. Technol. 46, 30–45 (2014)
Neff, J., Dickens, M.: 2016 public transportation fact book (2017)
Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)
Sengupta, S., Basak, S., Peters, R.A.: Data clustering using a hybrid of fuzzy c-means and quantum-behaved particle swarm optimization. In: 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), pp. 137–142, January 2018. https://doi.org/10.1109/CCWC.2018.8301693
Sengupta, S., Basak, S., Peters, R.A.: Particle swarm optimization: a survey of historical and recent developments with hybridization perspectives. Mach. Learn. Knowl. Extract. 1(1), 157–191 (2018). http://www.mdpi.com/2504-4990/1/1/10
Sengupta, S., et al.: A review of deep learning with special emphasis on architectures, applications and recent trends. CoRR abs/1905.13294 (2019). http://arxiv.org/abs/1905.13294
Sun, F., Samal, C., White, J., Dubey, A.: Unsupervised mechanisms for optimizing on-time performance of fixed schedule transit vehicles. In: 2017 IEEE International Conference on Smart Computing (SMARTCOMP), pp. 1–8. IEEE (2017)
Szeto, W.Y., Wu, Y.: A simultaneous bus route design and frequency setting problem for Tin Shui Wai, Hong Kong. Eur. J. Oper. Res. 209(2), 141–155 (2011)
Tibshirani, R., Walther, G., Hastie, T.: Estimating the number of clusters in a data set via the gap statistic. J. Roy. Stat. Soc.: Ser. B (Stat. Methodol.) 63(2), 411–423 (2001)
Ting, C.J., Schonfeld, P.: Schedule coordination in a multiple hub transit network. J. Urban Plann. Dev. 131(2), 112–124 (2005)
Wang, Y., Zhang, D., Hu, L., Yang, Y., Lee, L.H.: A data-driven and optimal bus scheduling model with time-dependent traffic and demand. IEEE Trans. Intell. Transp. Syst. 18(9), 2443–2452 (2017)
Wu, Y., Yang, H., Tang, J., Yu, Y.: Multi-objective re-synchronizing of bus timetable: model, complexity and solution. Transp. Res. Part C: Emerg. Technol. 67, 149–168 (2016)
Zhong, S., Zhou, L., Ma, S., Jia, N., Zhang, L., Yao, B.: The optimization of bus rapid transit route based on an improved particle swarm optimization. Transp. Lett. 10(5), 257–268 (2018). https://doi.org/10.1080/19427867.2016.1258972
Acknowledgments
This work is supported by The National Science Foundation under the award numbers CNS-1528799 and CNS-1647015 and 1818901 and a TIPS grant from Vanderbilt University. We acknowledge the support provided by our partners from Nashville Metropolitan Transport Authority.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Basak, S., Sun, F., Sengupta, S., Dubey, A. (2019). Data-Driven Optimization of Public Transit Schedule. In: Madria, S., Fournier-Viger, P., Chaudhary, S., Reddy, P. (eds) Big Data Analytics. BDA 2019. Lecture Notes in Computer Science(), vol 11932. Springer, Cham. https://doi.org/10.1007/978-3-030-37188-3_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-37188-3_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37187-6
Online ISBN: 978-3-030-37188-3
eBook Packages: Computer ScienceComputer Science (R0)