Bridging the Gap: How App-Based Mobility Data Complements Traditional Surveys for Smarter Transportation Planning

Urban mobility is at a crossroads. As cities worldwide strive to become more sustainable, inclusive, and data-driven, transportation planners and policymakers face a growing challenge: obtaining accurate, representative, and actionable mobility insights. Survey-based datasets like Germany’s MiD (Mobilität in Deutschland) have traditionally been the gold standard for mobility research. But with the rise of smartphone technology and app-based tracking, new doors are opening for richer, more granular insights into how people move.

So, how do these two methods compare, and can they work together? At MOTIONTAG, we believe that the future of mobility planning lies in the synergy between traditional and digital methodologies. Let’s explore how combining these data sources can empower governments, public transport authorities, and mobility consultants to make smarter decisions.

The Strengths of Traditional Mobility Surveys

Survey-based mobility data, such as MiD, has long provided a high-level picture of travel behavior. Its advantages include:

• Representativity: Survey data is weighted to reflect the entire population, ensuring a statistically sound basis for transport planning.
• Standardization: Consistent methodologies allow for long-term comparability and trend analysis for policy development at national and regional levels.
• Demographic Insights: Traditional surveys capture socio-economic factors, motivations, and qualitative feedback that raw movement data alone cannot provide.

However, survey data also has limitations. It relies on self-reported travel behavior, which introduces recall bias. It also captures only a single day’s snapshot, missing the nuances of mobility trends over time. Additionally, national surveys often lack the spatial granularity needed for targeted urban interventions.

MOTIONTAG experts highlight this issue: “Recall bias is a major challenge in survey-based mobility data. Participants often forget to report short walking or cycling trips, leading to an incomplete picture of urban mobility.”

The Advantages of App-Based Mobility Data

In contrast, app-based mobility datasets, like those collected through the Aachen Move App, use GPS-based tracking to capture continuous mobility patterns. This approach offers several benefits:

• High Granularity: Movement data is recorded in real-time, allowing for precise analysis of first- and last-mile trips, multimodal journeys, and peak travel times.
• Continuous Tracking: Unlike surveys, app-based datasets offer longitudinal insights that reveal behavioral changes over weeks, months, or even years.
• Cross-Border Data: In regions like Aachen, where commuters frequently travel between Germany, Belgium, and the Netherlands, app-based tracking provides crucial cross-border mobility insights that surveys typically overlook.
• Further aggregation levels: Analysis from GPS tracking data can be aggregated to many different customized levels to provide integrated mobility insights to cities, different groups of towns, districts, counties, or regions.

Yet, app-based data is not without its challenges. User selection bias can occur if tracking is voluntary or incentivized, potentially overrepresenting certain groups (e.g., environmentally conscious users). Additionally, while GPS data is highly detailed, it lacks the contextual information about user motivations that surveys provide.

According to Ferenc Kulcsar, Senior Data Analyst at MOTIONTAG, while Smartphone GPS-based tracking provides an excellent overview of movement patterns, it’s essential to integrate qualitative data from surveys to understand why people choose specific modes of transport.