Location-allocation and public transit

The problem

• UCL IOE (Institute of Education) run a teacher placement programme.
• These students are learning how to become high school teachers and have two 60 day placements at schools.
• They have about 800 students who need to be placed across about 500 schools, across about 5 subject groups.
• Currently they use a combination of Excel spreadsheets and Google Maps (locations and public transport routing) to do this process manually.
• This is very time consuming and would take at least a week per subject group to complete.

We know there is a better way

• Location-Allocation could be a solution to this problem.
  • Find locations for facilities from a set of demand locations.
• Classic example:
  • Where do we put a new hospital so as many residents as possible are within the shortest possible travel time?
  • E.g. ~ run drive-time routes from every house to every possible hospital location. Select the shortest total time.

Initial Results

• Working with James Grindrod - IOE Placement Manager
• Developed an automated way of doing this

What is Location Allocation?

• Has been around in GIS for a long time.

• Very commonly drive-time focused (or walk or cycle….)

• Relatively little in terms of public transport.

• In UCL IOE, public transport is key.

  • About 70% of students use public transport to get to their placement schools.

  • In general, ~8% of people use public transport to get to work Census 2021.

What is Location Allocation?

How did we do this?

• I was contacted by UCL IOE (James Grindrod) and UCL ARC (Tim Machin) to see if there was a better way.
• Many ways of doing location-allocation
  • I’m not an expert on the exact ins and outs of this
• Need to integrate public transport data
  • Can create an OD matrix from TfL API
• Need to work with a large number of students (~800) and schools (~500)
• Considered ArcGIS Pro, FLP Spreadsheet Solver, PySpatialOpt,
• Reached out via Twitter

How did we do this? phase 1

• Qunshan Zhao suggested spopt, part of PySAL, Python
• spopt couldn’t do what I wanted, but happy to develop
• Applied for AGILE funding to do this, successful (+ Glasgow):
  • Rongbo Xu added capacitated p-median location-allocation model to spopt
• UCL ISD supported first phase of development:
  • Patrick Roddy created code to calculate routes for student (postcode) to school (postcode) using TfL API

How did we do this? phase 2

UCL ISD developed a tool for IOE placements team to use

1. Upload
2. Input Data
3. Compute Journeys
4. Allocate
5. Review
6. Export

1. Upload

• Upload the data on students (placements) and schools as CSV.
• Student name, home postcode, transport modes, school restrictions, priority information related to caring, etc.
• School, location, priority, etc.

2. Input Data

• Input Data checks data and highlights any missing data.

3. Compute Journeys

• Uses TfL API to calculate all possible public transport journeys from each student to each school
• Returns a big OD matrix of journey time (mins), number of legs, number of modes
• 800 students and 500 schools takes about 26 hours to process.
• This only needs to be done once.
• Currently being optimised.

4. Allocate

• Can adjust allocation, ‘lock’ students and re-run.
• More-or-less in real time.

4. Allocate

• Can adjust allocation, ‘lock’ students and re-run.
• More-or-less in real time.

5. Review