Smart mobility is often assessed through congestion relief and travel-time indicators, but urban managers operate within a coupled system in which transport operations, digital infrastructure, and energy goals shape one another. The coupling becomes harder to ignore as electrification expands and as cities are asked to report carbon outcomes alongside service reliability in reporting cycles. Analytics-enabled control may lower energy use indirectly when stop-start driving is reduced, average speeds are stabilised, and bus operations become more predictable, which changes traction energy demand and idling losses. At the same time, sensors, communications, and cloud-edge processing draw power continuously, and their lifecycle costs are borne by transport agencies even when energy reporting focuses on vehicles alone.
This study treats city-scale intelligent transportation platforms as an energy-aware urban service system and examines how energy considerations are translated into management practice. A comparative case-study design is applied to Hangzhou, Singapore, and Kuala Lumpur, where different delivery logics (platform-centric public–private delivery, government-led reliability management, and multi-agency coordination under compound risks) have been used to scale data-driven operations. Evidence is triangulated from policy documents, implementation reports, and peer-reviewed studies, and it is interpreted with attention to indicator definitions, baselines, and reporting scope.
What makes similar tools produce different energy and service outcomes? Three management mechanisms are traced. One concerns the local performance regime: which targets are set (delay, reliability, energy or CO2), how objectives are weighted, and how distributional effects are checked when reported gains concentrate on selected corridors or user groups. Another concerns lifecycle governance for digital infrastructure. Low-latency operation is often supported through edge deployment near intersections, yet distributed hardware increases power draw and maintenance work, especially when models must be monitored and updated under drift. A third mechanism concerns coordination with energy actors, since EV charging peaks, pricing, and demand response can be affected by routing and signal policies that shift load in time and place.
Reported differences are interpreted through these managerial conditions, while recurring constraints are acknowledged. Metric heterogeneity, vendor dependency, and limited independent benchmarking reduce cross-city comparability and make causal claims difficult to sustain when evaluation windows and definitions vary.
