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International Conference on Big Data Analytics

BDA 2019: Big Data Analytics pp 265–284Cite as

Data-Driven Optimization of Public Transit Schedule

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11932))

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.

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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.

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Authors and Affiliations

  1. Department of EECS, Vanderbilt University, Nashville, TN, USA

    Sanchita Basak, Fangzhou Sun, Saptarshi Sengupta & Abhishek Dubey

Authors
  1. Sanchita Basak
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  2. Fangzhou Sun
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  3. Saptarshi Sengupta
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  4. Abhishek Dubey
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Corresponding author

Correspondence to Abhishek Dubey .

Editor information

Editors and Affiliations

  1. Missouri University of Science and Technology, Rolla, MO, USA

    Sanjay Madria

  2. Harbin Institute of Technology, Shenzhen, China

    Philippe Fournier-Viger

  3. Ahmedabad University, Ahmedabad, India

    Sanjay Chaudhary

  4. International Institute of Information Technology, Hyderabad, India

    P. Krishna Reddy

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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

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  • DOI: https://doi.org/10.1007/978-3-030-37188-3_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37187-6

  • Online ISBN: 978-3-030-37188-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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