MUTra-CDMX: Multisource Urban Traffic Dataset for the Insurgentes Sur Corridor in Mexico City

The growing complexity of urban mobility requires datasets that integrate dynamic traffic observations with meteorological, geometric, and urban-context information. This study presents MUTra-CDMX, a multisource urban traffic dataset covering a 14.72 km section of the Insurgentes Sur corridor in Mexico City. Traffic data were obtained from TomTom at five-minute intervals for 20 consecutive road segments from 1 November 2024 to 28 February 2025. Hourly meteorological data were retrieved from Meteosource, while segment-level geometry, topology, signalized locations, and nearby points of interest were derived from TomTom metadata and OpenStreetMap. The primary analytical file contains 691,200 segment–timestamp records and 12 variables describing traffic and free-flow conditions, meteorological information, derived operational indicators, and reconstruction status. Of these records, 682,264 are original observations and 8936 are reconstructed segment–timestamp combinations, identified by the Boolean variable is_imputed. Technical validation confirmed complete temporal coverage, preservation of original traffic observations, consistent weather alignment, and reconstruction performance through artificial masking. Predictive utility was evaluated through chronological travel-time forecasting under a leakage-controlled protocol. At the 30 min horizon, XGBoost achieved a mean absolute error of 12.84 s, a root mean squared error of 37.91 s, and a coefficient of determination (R2) of 0.771, outperforming a persistence baseline. MUTra-CDMX supports congestion analysis, imputation studies, spatiotemporal modeling, and travel-time forecasting.