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Bus Routing Optimization Helps Boston Public Schools Design Better Policies

Author

Listed:
  • Dimitris Bertsimas

    (Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139;)

  • Arthur Delarue

    (Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139;)

  • William Eger

    (Boston Public Schools, Roxbury, Massachusetts 02119)

  • John Hanlon

    (Boston Public Schools, Roxbury, Massachusetts 02119)

  • Sebastien Martin

    (Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139;)

Abstract

In the winter of 2016, Boston Public Schools (BPS) launched a crowdsourcing national competition to create a better way to construct bus routes to improve efficiency, deepen the ability to model policy changes, and realign school start times. The winning team came from the Massachusetts Institute of Technology (MIT). The team developed an algorithm to construct school bus routes by assigning students to stops, combining stops into routes, and optimally assigning vehicles to routes. BPS has used this algorithm for two years running; in the summer of 2017, its use led to a 7% reduction in the BPS bus fleet. Bus routing optimization also gives BPS the unprecedented ability to understand the financial impact of new policies that affect transportation. In particular, the MIT research team developed a new mathematical model to select start times for all schools in the district in a way that considers transportation. Using this methodology, BPS proposed a solution that would have saved an additional $12 million annually and also shifted students to more developmentally appropriate school start times (e.g., by reducing the number of high school students starting before 8:00 a.m. from 74% to 6% and the average number of elementary school students dismissed after 4:00 p.m. from 33% to 15%). However, 85% of the schools’ start times would have been changed, with a median change of one hour. This magnitude of change led to strong vocal opposition from some school communities that would have been affected negatively; therefore, BPS did not implement the plan.

Suggested Citation

  • Dimitris Bertsimas & Arthur Delarue & William Eger & John Hanlon & Sebastien Martin, 2020. "Bus Routing Optimization Helps Boston Public Schools Design Better Policies," Interfaces, INFORMS, vol. 50(1), pages 37-49, January.
  • Handle: RePEc:inm:orinte:v:50:y:2020:i:1:p:37-49
    DOI: 10.1287/inte.2019.1015
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    References listed on IDEAS

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    1. Dimitris Bertsimas & Arthur Delarue & Sebastien Martin, 2019. "Optimizing schools’ start time and bus routes," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 116(13), pages 5943-5948, March.
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    Cited by:

    1. Dan Bumblauskas & Amy Igou & Salil Kalghatgi & Cole Wetzel, 2022. "Public Policy and Broader Applications for the Use of Text Analytics During Pandemics," Interfaces, INFORMS, vol. 52(6), pages 568-581, November.
    2. Mariana Laverde, 2022. "Distance to Schools and Equal Access in School Choice Systems," Working Papers 2022-002, Human Capital and Economic Opportunity Working Group.
    3. Haoyuan Hu & Ying Zhang & Jiangwen Wei & Yang Zhan & Xinhui Zhang & Shaojian Huang & Guangrui Ma & Yuming Deng & Siwei Jiang, 2022. "Alibaba Vehicle Routing Algorithms Enable Rapid Pick and Delivery," Interfaces, INFORMS, vol. 52(1), pages 27-41, January.
    4. Mariana Laverde, 2022. "Distance to Schools and Equal Access in School Choice Systems," Boston College Working Papers in Economics 1046, Boston College Department of Economics.

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