Reducing Emissions Through AI‐Driven Multimodal Transport Optimization in IoT‐Connected Environments

The cities are the largest emitters of the greenhouse gas (GHG) emissions in the world, with close to a quarter of the total energy‐related CO 2 emissions occurring in urban places. As urbanization and demand to move surge in transiting, traditional monomodal transport systems, especially the ones that rely on the use of personal vehicles, are inefficient given the low efficiency, and thus unaffordable to support the goal of sustainability. The current article suggests the idea of an artificial intelligence (AI)‐powered, Internet of Things (IoT)‐compatible real‐time multimodal optimization tool to minimize urban emissions yet improve the efficient mobility of people and goods. The system unites AI algorithms, such as reinforcement learning (RL) and graph neural networks (GNNs), and the data streams based on the IoT devices connected to road sensors, GPS, mobile apps, and the public transportation systems. It actively suggests the best paths and combinations of methods of transport (e.g., walking, biking, bus, metro) according to the traffic, weather, user preferences, and emission characteristics in real time. Through simulations that have been carried out in the simulation of urban mobility (SUMO) platform on a modeled city, average CO 2 emissions per trip have been reduced by 45% compared with baseline routing strategies. Further findings indicate the use of suggested alternatives with high user acceptance rate (83%) and efficient modal shifts of the avoided use of a private car. The suggested framework provides a data‐driven and scalable framework of sustainable urban mobility. Considering that it integrates modeling of emissions into the central optimization engine, enables real‐time, personalized decision‐making, it can help achieve climate action as well as aid in the smart city development. The proposed AI–IoT framework demonstrably reduces CO 2 emissions and supports the United Nations Sustainable Development Goals (SDGs), specifically SDG 11 (cities, targets 11.2/11.6), SDG 13 (climate, target 13.2), and SDG 9 (infrastructure, targets 9.1/9.4) with secondary alignment to SDG 7.3 on energy efficiency.