A methodological evaluation of app location data extraction and processing for traffic flow applications

Emerging forms of spatial data, such as sensor-based location data collected by mobile apps (MPD), have shown promise in enhancing or replacing traditional analytical methods. MPD provides detailed and timely information at lower costs, making it valuable for urban and transport planning. Multi-source MPD also offers advantages, such as greater representativeness. However, the high volume, variable frequency, and heterogeneity pose significant challenges in data processing and analysis. This study investigates the potential of multi-source MPD to estimate traffic volumes at the street level, focusing on Glasgow city-region. The evaluation covers a range of processing approach combinations. These include three MPD spatial extraction techniques —Simple Buffer (SB), Connected Street Buffer (CB), and Entire Street Buffer (ESB) —to filter relevant vehicular movement data. Additionally, four processing approaches are considered: raw counts (A1), simplified counts with commuter assumptions (A2), detailed geometric analysis (A3), and map matching (A4). The results are compared to manual traffic counts from the Department for Transport (DfT). The findings reveal that raw MPD counts (A1) lead to important biases due to uneven data volume per user. Simplified counts (A2) improve accuracy but still capture non-vehicular activities. Map matching (A4) introduces substantial improvements compared to A1 and A2. However, this approach proves less effective than A3. The geometric-temporal approach (A3) offers the most accurate estimates by incorporating movement inferences. Buffer size and built environment factors influence the methods’ performance, highlighting the need for localised buffers and built environmental controls. This research finds moderate potential for multi-source MPD to supplement traditional traffic systems, providing cost-effective and detailed traffic insights at least from a spatial variability perspective, but not for cross-temporal comparisons. Future studies can extend MPD applications for active travel and extend the analysis to other cities.