Improving transportation research with passively-collected location data

Danielle McCool Peter Lugtig, Barry Schouten, Ole Mussmann

2019-03-04

Sensor data projects from WIN

  • Travel/mobility
  • Time use
  • Budget expenditure
  • Fitness/physical activity

This projet

We record always-on passive location data

from mobile devices

for the Ministry of Infrastructure

to supplement existing travel diary studies

to replace existing travel diary studies

CBS travel app

CBS in-house programmer (intern)

Field test 11/2018 – 12/2018

1902 letters

CBS field test data

Data being digested by:

  • 2 Research Masters students
  • 3ish interns
  • Handful of programmers
  • At least two professors
  • 1 PhD student

Stops

stop1

User-supplied motive

User-supplied name

Tracks

track1

Only get a transport mode

🚴 🚗 🚁

Important bits

Device information

Location data

Track data

Stop data

Daily Questions

Important bits

  • Location data
  • Track data
  • Stop data

Location data

  • High-tracking mode: 1 measurement per second
  • Low-tracking mode: 1 measurement per minute

Track data

  • Start time
  • Stop time
  • Transportation mode

Stop data

  • Start time
  • Stop time
  • Stop name
  • Stop motive

Interesting challenges

Incomplete data

Device differences

Strange sensor measurements

Sensitivity vs. battery life

What is a stop

What is a stop?

test

What is a stop (lvl 2)

“home”

What is a stop (lvl 3)

work

Not a stop

  • Waiting at a stoplight
  • Being stuck in traffic
  • Switching Wi-Fi on and having your position change

A stop

  • Going from one building to another on campus
  • Taking your dog to the dog park
  • Dropping your kid off at school

???

  • Waiting for your train at the station
  • Taking your dog for a walk
  • Going to ask the neighbors for your package

Our stop definition

Two levels

  1. Data collection
  2. User interface

Data collection

Parameters trigger ‘high-tracking’ and ‘low-tracking’ modes on the device.

  1. Distance Delta Limit
  2. Time period within that radius

User interface

  1. Grouping radius
  2. Time
  3. Minimum Stop Accuracy
  4. Stop merge radius
  5. Stop merge max travel radius

Grouping radius parameter

work

Time parameter

work

Better interpretation

work

Missing data

Missing data occurs at myriad levels within this data.

  1. Recruitment (Willingness)
  2. App/device incompatibility (?)
  3. App installation (Compliance)
  4. App closes itself (?)
  5. App only has location on Wi-Fi or GPS
  6. Device dies
  7. Short losses due to tunnels or buildings
  8. Filling in the user-generated data (Attrition)

Missing data in the recruitment phase

recruitment

Missing data over time per OS

recruitment

Missing data within a day

recruitment

Missing data within a trip

recruitment

Next steps

PhD project consists of five projects (2018-2021):

  • A descriptive paper over the app
  • Adjustment for missing data in CBS verplaatsingen app
  • Adjustment for measurement error/inaccurate measurements in CBS verplaatsingen app
  • Two projects linked to time-use sensor data

Thanks! Questions?

Summary
  • Goal: critically examine app as diary replacement
  • Field test had over 600 respondents
  • Self-reported stops are difficult to reproduce programmatically
  • Lots of flavors of missingness
  • Immediate issues: reporting distance to the stakeholders

Like to know more?