Merging self-reported with technically sensed data for tracking mobility behavior in a naturalistic intervention...

Area of Science:

• Environmental Health Sciences
• Transportation Studies
• Public Health Interventions

Background:

• Accurate sound exposure data is vital for intervention studies, especially in real-world mobility settings.
• Traditional self-reported data lacks precision, while wearable devices are typically used for short-term studies.
• Long-term, naturalistic intervention studies require robust methods for collecting reliable mobility exposure data.

Purpose of the Study:

• To integrate self-reported and technically sensed mobility data for enhanced accuracy in the GISMO long-term intervention study.
• To provide more reliable exposure data by combining two distinct data collection methods.
• To analyze modal split and travel time differences between control and intervention groups.

Main Methods:

• Employed spatio-temporal data matching procedures to combine self-reported and sensed mobility data.
• Validated the accuracy of the combined data against a corrected reference, noting self-reported data deviations of ±10%.
• Analyzed modal split statistics and annual mean travel times for control (CG) and intervention groups (IG-PT, IG-C).

Main Results:

• The combined data approach achieved higher accuracy for mobility exposure data.
• Intervention groups (IG-PT, IG-C) showed a significant reduction in car travel (9.7-10.3%) compared to the control group (73.7%).
• Annual mean travel times were substantially higher in intervention groups compared to the control group, with no winter shift towards car use.

Conclusions:

• Combining self-reported and sensed mobility data via spatio-temporal matching significantly improves data reliability for naturalistic intervention studies.
• The GISMO study demonstrated a successful reduction in car dependency in intervention groups.
• While no single method is perfect, data fusion offers a substantially improved solution for collecting exposure data in mobility research.