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@article{186378,
        author = {Mohini Bharat Todkari},
        title = {Real-Time Geospatial Data Integration for High- Accuracy Predictive Analytics in Urban Mobility and Autonomous Systems},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {1278-1284},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186378},
        abstract = {Urban mobility and autonomous systems increasingly rely on real-time geospatial data integration to enable high-accuracy predictive analytics essential for traffic management, safety, and efficiency. This review synthesizes advances in data acquisition, fusion techniques, and AI-driven predictive models that harness heterogeneous data sources such as GPS, IoT sensors, and satellite imagery. We highlight recent hybrid machine learning frameworks that have significantly improved prediction accuracy in dynamic urban environments, while addressing challenges such as latency, data heterogeneity, and privacy. The review also explores emerging technologies like edge computing and privacy-preserving analytics, framing them within the context of scalable urban mobility solutions. Future research directions emphasize adaptive AI models, enhanced data interoperability, and ethical considerations to realize fully autonomous and smart cities. This paper serves as a comprehensive reference for researchers and practitioners aiming to advance geospatial analytics for urban mobility and autonomous systems.},
        keywords = {Real-time geospatial data, Urban mobility, Autonomous systems, Predictive analytics, Data fusion, Machine learning, Edge computing, Privacy-preserving analytics.},
        month = {November},
        }