Bus Routing Optimization Helps Boston Public Schools Design Better Policies.
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| Title: | Bus Routing Optimization Helps Boston Public Schools Design Better Policies. |
|---|---|
| Authors: | Bertsimas, Dimitris (AUTHOR), Delarue, Arthur (AUTHOR), Eger, William (AUTHOR), Hanlon, John (AUTHOR), Martin, Sebastien (AUTHOR) |
| Source: | INFORMS Journal on Applied Analytics. Jan/Feb2020, Vol. 50 Issue 1, p37-49. 13p. |
| Subjects: | Massachusetts Institute of Technology, School children, Public schools, School rankings, Bus travel, Bus stops |
| Abstract: | The authors discuss how Boston Public Schools (BPS) and a team from Massachusetts Institute of Technology addressed the BPS bus routing problem. They developed an algorithm that decomposes and separately solves subproblems of assigning students to bus stops, assigning stops to bus trips, and connecting trips into an itinerary for each bus. 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. [ABSTRACT FROM AUTHOR] |
| Copyright of INFORMS Journal on Applied Analytics is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: pbh DbLabel: Psychology and Behavioral Sciences Collection An: 142690636 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Bus Routing Optimization Helps Boston Public Schools Design Better Policies. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Bertsimas%2C+Dimitris%22">Bertsimas, Dimitris</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Delarue%2C+Arthur%22">Delarue, Arthur</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Eger%2C+William%22">Eger, William</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hanlon%2C+John%22">Hanlon, John</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Martin%2C+Sebastien%22">Martin, Sebastien</searchLink> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22INFORMS+Journal+on+Applied+Analytics%22">INFORMS Journal on Applied Analytics</searchLink>. Jan/Feb2020, Vol. 50 Issue 1, p37-49. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Massachusetts+Institute+of+Technology%22">Massachusetts Institute of Technology</searchLink><br /><searchLink fieldCode="DE" term="%22School+children%22">School children</searchLink><br /><searchLink fieldCode="DE" term="%22Public+schools%22">Public schools</searchLink><br /><searchLink fieldCode="DE" term="%22School+rankings%22">School rankings</searchLink><br /><searchLink fieldCode="DE" term="%22Bus+travel%22">Bus travel</searchLink><br /><searchLink fieldCode="DE" term="%22Bus+stops%22">Bus stops</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The authors discuss how Boston Public Schools (BPS) and a team from Massachusetts Institute of Technology addressed the BPS bus routing problem. They developed an algorithm that decomposes and separately solves subproblems of assigning students to bus stops, assigning stops to bus trips, and connecting trips into an itinerary for each bus. 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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of INFORMS Journal on Applied Analytics is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1287/inte.2019.1015 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 37 Subjects: – SubjectFull: Massachusetts Institute of Technology Type: general – SubjectFull: School children Type: general – SubjectFull: Public schools Type: general – SubjectFull: School rankings Type: general – SubjectFull: Bus travel Type: general – SubjectFull: Bus stops Type: general Titles: – TitleFull: Bus Routing Optimization Helps Boston Public Schools Design Better Policies. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Bertsimas, Dimitris – PersonEntity: Name: NameFull: Delarue, Arthur – PersonEntity: Name: NameFull: Eger, William – PersonEntity: Name: NameFull: Hanlon, John – PersonEntity: Name: NameFull: Martin, Sebastien IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan/Feb2020 Type: published Y: 2020 Identifiers: – Type: issn-print Value: 26440865 Numbering: – Type: volume Value: 50 – Type: issue Value: 1 Titles: – TitleFull: INFORMS Journal on Applied Analytics Type: main |
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