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

Emerging forms of spatial data, such as mobile phone-based location data (MPD), have shown promise in enhancing or replacing traditional analytical methods. MPD provides detailed, timely information at lower costs, making it valuable for urban and transport planning. However, the high volume and heterogeneity of MPD pose significant challenges in data processing and analysis. This study investigates the potential of MPD to estimate traffic volumes at the street level, focusing on Glasgow city-region. We evaluate three MPD spatial extraction techniques—Simple Buffer (SB), Connected Street Buffer (CB), and Entire Street Buffer (ESB)—to filter relevant vehicular movement data. Additionally, we assess three processing approaches: raw counts (A1), simplified counts with commuter assumptions (A2), and detailed spatio-temporal analysis (A3). 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. The spatio-temporal approach (A3) offers the most accurate estimates by incorporating movement inferences. However, buffer size and built environment factors significantly impact the methods' performance, highlighting the need for localised buffers and built environmental controls. This research demonstrates MPD's potential to supplement traditional traffic systems, providing cost-effective and detailed traffic insights. Future studies can extend MPD applications for active travel and extend the analysis to other cities.