A meta-synthesis of the big data in urban transportation planning processes

This study conducts a qualitative meta-synthesis to examine how big data has transformed urban transportation planning processes. Drawing upon the pre-analysis, technical analysis, and post-analysis phases, the review classifies data sources into four overarching groups (i.e., Mobility Sensing, Infrastructure and Environment, Social and Behavioral, and Synthesized/Simulated) and identifies five analytical mechanisms through which big data generates planning value. The findings reveal that Mobility Sensing and Infrastructure and Environment datasets dominate the pre- and technical stages, while Social and Behavioral and Synthesized/Simulated data are most prevalent in post-analysis, reflecting a shift from data collection toward integration and feedback. Mechanistically, big data enhances planning through data processing and extraction, analytical and modeling approaches, real-time monitoring and adaptive control, decision-support and knowledge translation, and institutional and governance enablers. Across all stages, hybrid methodologies that combine traditional surveys with large-scale digital traces prove most effective, balancing behavioral interpretability with temporal precision. Nonetheless, challenges persist, including representativeness bias, the decline of traditional survey data, and institutional fragmentation in data governance. The study concludes that big data's value lies not in replacing conventional methods but in complementing them through multi-level fusion across data, feature, and decision layers. Future urban transportation planning will therefore depend on integrative, multi-source frameworks that combine technical innovation with ethical, equitable, and transparent governance, enabling continuous learning and adaptive policymaking in data-driven cities.