ISyE Seminar - Julia Yan *CANCELLED*

Event Details
  • Date/Time:
    • Thursday January 16, 2020
      11:00 am - 12:00 pm
  • Location: ISyE Main Room 228
  • Phone:
  • URL:
  • Email:
  • Fee(s):
    N/A
  • Extras:
Contact

Title: From data to decisions in urban transit and logistics

Abstract:

The Americans with Disabilities Act of 1990 mandates that door-to-door transit options be provided to those who cannot use the regular transit system due to disability. Paratransit agencies operate fleets of vehicles to fulfill daily requests for transportation, which are collected one to seven days ahead of time. Although paratransit is an essential safety net, it is also expensive to operate and requires large government subsidies. These financial difficulties, combined with significant improvements in integer optimization solvers in recent years, have let to interest in developing large-scale optimization algorithms for paratransit. We provide a cluster-then-optimize approach to servicing paratransit requests subject to labor constraints; this approach shows strong performance while also being tractable for daily use. Our case study is based on real data from Boston, MA ranging from 3,000 to 7,000 requests per day, and our algorithms improve upon Boston’s current state by over 30%.

The second part of the talk concerns inference of transit demand data, which is an essential input to any decision model. It is sometimes possible to track anonymized users through their commutes, accomplished through previous studies on smart cards, license plates, and mobile phones.  However, widely-available data sources are frequently in aggregated forms such as entry and exit counts, and one must recover the original demand from these aggregated counts. Such problems are generally underspecified. To address this, we present an optimization framework to recover origin-destination matrices under minimal assumptions, incorporating reasonable physical constraints such as flow conservation, smoothness, and symmetry. The proposed method is evaluated and shows strong improvement over the maximum entropy method on a variety of real-world data sets from Boston, New York City, and San Francisco, comprising tens to hundreds of stations.

 

Bio: Julia Yan is a fifth-year PhD student at the Operations Research Center at MIT, advised by Dimitris Bertsimas.  She is interested in large-scale, data-driven optimization, and is especially motivated by applications to urban operations and the public good. Prior to coming to MIT, Julia spent two years in operations consulting.  She completed her undergraduate degree at Princeton University in 2013.

Summaries

Summary Sentence: From data to decisions in urban transit and logistics

Full Summary: Abstract: The Americans with Disabilities Act of 1990 mandates that door-to-door transit options be provided to those who cannot use the regular transit system due to disability. Paratransit agencies operate fleets of vehicles to fulfill daily requests for transportation, which are collected one to seven days ahead of time. Although paratransit is an essential safety net, it is also expensive to operate and requires large government subsidies. These financial difficulties, combined with significant improvements in integer optimization solvers in recent years, have let to interest in developing large-scale optimization algorithms for paratransit. We provide a cluster-then-optimize approach to servicing paratransit requests subject to labor constraints; this approach shows strong performance while also being tractable for daily use. Our case study is based on real data from Boston, MA ranging from 3,000 to 7,000 requests per day, and our algorithms improve upon Boston’s current state by over 30%. The second part of the talk concerns inference of transit demand data, which is an essential input to any decision model. It is sometimes possible to track anonymized users through their commutes, accomplished through previous studies on smart cards, license plates, and mobile phones.  However, widely-available data sources are frequently in aggregated forms such as entry and exit counts, and one must recover the original demand from these aggregated counts. Such problems are generally underspecified. To address this, we present an optimization framework to recover origin-destination matrices under minimal assumptions, incorporating reasonable physical constraints such as flow conservation, smoothness, and symmetry. The proposed method is evaluated and shows strong improvement over the maximum entropy method on a variety of real-world data sets from Boston, New York City, and San Francisco, comprising tens to hundreds of stations.

Additional Information

In Campus Calendar
No
Groups

H. Milton Stewart School of Industrial and Systems Engineering (ISYE)

Invited Audience
Faculty/Staff, Postdoc, Public, Graduate students, Undergraduate students
Categories
Seminar/Lecture/Colloquium
Keywords
No keywords were submitted.
Status
  • Created By: sbryantturner3
  • Workflow Status: Published
  • Created On: Dec 17, 2019 - 8:37am
  • Last Updated: Jan 14, 2020 - 2:52pm