AI-Driven Data Analytics for IoT-Based Urban Mobility Solutions
DOI:
https://doi.org/10.48314/ceti.v1i2.32Keywords:
AI, IoT, Urban mobility, Data analytics, Deep learning, Machine learning, Big data, Public transportationAbstract
Urban mobility systems are increasingly dependent on Internet of Things (IoT) devices to gather real-time information on traffic, public transport, and parking. Data analytics techniques driven by AI can harness the potential of this information to enhance efficiency, sustainability, and user satisfaction. This paper examines the main applications of AI in urban mobility, including traffic management, the optimization of public transport, and intelligent parking solutions. We investigate the fundamental elements of IoT-based urban mobility systems, such as data collection, storage, and processing. In addition, we address the challenges and opportunities present in this domain, emphasizing the necessity for strong data privacy and security protocols. By utilizing advanced data analytics techniques, cities can uncover insights into urban mobility behaviors, facilitating informed decision-making and innovative strategies. Future research avenues include examining the integration of AI with emerging technologies like autonomous vehicles and edge computing to further transform urban transportation.
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