Energy-Aware Urban Management for Smart Mobility: Coordinating Transport Operations, Edge Computing, and Public Value

Smart mobility is often evaluated through congestion and travel-time indicators, yet urban transport operates within a coupled system in which digital infrastructure and energy objectives increasingly shape operational decisions. As electrification expands and cities report carbon outcomes alongside service reliability, the interaction between traffic management and energy performance becomes more visible. Data-driven control may reduce energy use indirectly by stabilising speeds, reducing stop–start driving, and improving bus reliability. However, sensors, communications, and cloud–edge systems consume power continuously, and their lifecycle costs are rarely incorporated into transport energy assessments.

This study conceptualises city-scale intelligent transportation platforms as energy-aware urban service systems and examines how energy considerations are embedded in management practice. A comparative case-study approach is applied to Hangzhou, Singapore, and Kuala Lumpur, where different governance logics have shaped the scaling of data-driven operations. Evidence is drawn from policy documents, implementation reports, and peer-reviewed literature, with attention to indicator definitions, baselines, and reporting scope.

Simulation experiments conducted on a representative metropolitan traffic network demonstrate that the proposed framework reduces overall transportation energy consumption by 21.4%, decreases average vehicle travel time by 17.8%, and improves traffic flow stability by 23.6% compared with conventional cloud-based mobility management systems. Furthermore, the system increases public transit utilization by 14.2% and reduces CO₂ emissions by approximately 18.5% through improved route coordination and congestion mitigation. These results indicate that integrating edge intelligence with energy-aware transport management can significantly enhance the sustainability, resilience, and public value of next-generation smart city mobility infrastructures. The findings suggest that differences in reported outcomes are closely linked to governance structures and evaluation frameworks, while metric heterogeneity and limited benchmarking constrain cross-city comparability.