Workshop B4' The Collection and Processing of Survey Data Using Mobile Technologies

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Workshop B4. The Collection and Processing of
Survey Data Using Mobile Technologies
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Workshop Participants

• Workshop Chair Jean Wolf, United
  StatesResource Paper Author Peter Stopher,
  AustraliaDiscussant Barbara Noble, United
  Kingdom
• Rapporteur Sean Doherty, Canada
• Contributing Authors
• Stephan Krygsman, South Africa
• Maat Kees,The Netherlands
• Nadine Schüssler, Switzerland
• Other Participants

• Notes
• Sizable group (n28)
• High attendance and participation rates
• No incentives offered

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Workshop Scope

• Really did focus on data collection and
  processing using mobile technologies
• Tried to stay away from non-response
• Limited discussion on broader study design

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State of the Art - Devices Data Collection

• Device evolution is heading to wearables, but
  vehicle-based studies may still have a role
• Most studies include GPS and diary, but trend
  towards GPS only
• Interview and delivery deployment methods vary
• Viable alternatives to GPS are being evaluated
• Mobile/cell phone options (now) Also used for
  CAPI
• Bluetooth / WiFi / RFID / smartcard / RDS (in
  research)
• Alternatives have different levels of detail,
  precision, cost, coverage
• Mixed modes (tech and non-tech) could be used
• Select methods and solution(s) based on purpose /
  need

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State of the Art Data Processing

• Various algorithms developed to identify key
  diary elements
• stops, trips OD / route / distance / start time
  / duration
• travel mode
• trip purpose
• Use of GIS datasets important (road network,
  transit network, points of interest, land use,
  etc.)
• Still largely in exploratory phase
• Some implementing rule based, others using fuzzy
  logic
• Little validation and little ground truth to do
  it
• Prompted recall interfaces developed and
  implemented, but burden is an issue
• Mobile/cell phone research showing promise

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Issues - Devices and Data Collection

• Deploying/retaining devices
• On/off switch desirable ?
• Logging rules (e.g., frequency, speed screen)
• Missing data / messy data
• Age cut off for deployment (practicality and
  ethics)
• Reducing bias
• Encryption/security
• Ethics
• Avoiding lawsuits
• Retaining/archiving raw data
• Cost of cell-based location for large samples
• Practicality of large scale location-enabled
  mobile phones (commercial considerations)
• How best to recruit and communicate with subjects
  

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Issues - Data Processing

• Lack of available software commercial,
  share/free ware
• Are we ready to standardize?
• Data Collection (NMEA parsing)
• Data Storage (XML desirable)
• Algorithms
• Calibration/validation data essential, but
  challenging to acquire
• Prompted recall offers some potential
• Direct observation may be needed
• Assessing respondent burden from prompted recall
• Different methods used, e.g., fuzzy logic, AI,
  rule-based,

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Research Needs Data Collection

• Continued improvements in mobile technology
  devices
• Functionality, cost, power capacity, storage
  capacity, etc.
• Deployment method analyses with respect to study
  purpose, cost, response rates
• Continued research on mobile phone tower
  location traces for travel surveys
• Feasibility study for deployment of custom
  software for population-based mobile phone user
  sample
• Possible joint venture research government,
  private, university (e.g., health or tourism
  research)
• How young should or could participants be?
• Impact of Galileo on accuracy and coverage?

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Research Needs Data Processing

• Standard data, standard processing software, or
  perhaps algorithm modules
• Commercial or free processing software (are we
  ready?)
• Comparative analysis of accuracy of mode and
  purpose identification
• Development of travel companion (who) / party
  size estimators (and is this really needed?)
• Development of validation datasets
• Development of algorithms / software independent
  of GIS
• Overall methodology (GPS/CATI, GPS/PR, GPS only)
  tradeoff analysis (burden, quality, bias, cost).
  Same for mobile phones.
• Pushing the modelling paradigms (e.g., number of
  travel days, number of persons per household,
  data elements, travel time measurements)

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