Spatiotemporal Analysis of Urban Traffic Patterns Using Floating Car Data: A Methodology for Day-Type and Weather Baselines in Budapest

GPS-derived floating car data (FCD) provide spatially continuous urban traffic observations without fixed-sensor infrastructure. This study develops a spatiotemporal baseline framework jointly modelling day type and precipitation for 1189 junction-level nodes in Budapest. A six-phase pipeline—GPS preprocessing, coordinate reprojection, FME (Feature Manipulation Engine, Safe Software Inc., Surrey, BC, Canada)-based map-matching, junction-level aggregation, Voronoi meteorological allocation, and dataset assembly—was applied to 44.1 million 10 s records from approximately 1100 probe vehicles (November 2024–December 2025). Public holidays form a structurally distinct traffic flow pattern compared to Sundays (r = 0.71) and to regular workdays (r = 0.42); morning peak shifts to 09:00–11:00 and pooling holidays with Sundays introduces reference errors of 15–25%. Precipitation raises morning peak volumes by 6–17% across all zones while afternoon peaks remain statistically unchanged, consistent with commuter inertia; Saturday volumes fall by 7–15%. Rainy Wednesdays reach 109–112% of the Monday dry reference in inner zones, attributed to hybrid workers advancing their office day. Pairwise junction correlations show a non-monotonic distance-decay pattern, and time-lagged cross-correlation identifies 23 anticipative junction pairs with 60–90 min lead times. The results could potentially help decision making when developing city-wide infrastructure and tuning traffic signals so that traffic can be optimised and adapt to both real-time natural and social effects. The resulting baselines map onto DATEX II (Data Exchange standard, CEN EN 16157) ElaboratedDataPublication fields, supporting metadata publication on the Hungarian National Access Point under EU Regulation 2022/670/EU.