From Congestion Ranking to Sustainable Urban Mobility Diagnosis: A Multidimensional Floating-Car-Data Assessment of Malaysian Cities

Urban mobility assessment requires evidence on congestion, travel time, speed loss, temporal variability, and peak imbalance. However, many city comparisons still rely on a single congestion ranking, which can obscure realised travel-time burden and road-network context. This study proposes a multidimensional diagnostic framework for seven Malaysian cities by integrating TomTom floating-car-data benchmarks, OpenStreetMap road-network indicators, and contextual population–vehicle background proxies. The framework applies data checking, unit harmonisation, direction adjustment, z-score standardisation, sensitivity analysis, and Ward hierarchical clustering. The results identify three exploratory profiles: high-congestion and long-travel-time cities, a city with high state-level vehicle-background context and strong peak asymmetry, and mixed lower-overall-pressure cities. Given the small sample, these profiles are interpreted as exploratory diagnostic patterns rather than stable city classifications. The framework supports sustainable mobility screening in data-scarce urban contexts.