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@article{202978,
        author = {Avichal Saxena},
        title = {Enhancing Sustainable Urban Mobility: A Multi-Module DataDriven Framework for Intelligent Transportation Analysis and Policy Support},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {9474-9479},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202978},
        abstract = {Sustainable urban transportation stands as one of the most pressing challenges facing modern cities. As populations swell and vehicle ownership rises, cities grapple with chronic traffic congestion, soaring energy consumption, deteriorating air quality, and mounting economic losses. Traditional manual traffic analysis methods have become inadequate in the face of today's high-velocity, multi-source data streams. This paper introduces UrbanFlow AI, a comprehensive, integrated, multi-module analytical framework that fuses advanced statistical analysis, interactive data visualization, and predictive machine learning to deliver a 360-degree view of urban transport dynamics. Built upon a rich simulated urban traffic dataset containing over 1.2 million records across six fictional cities, the system incorporates six concurrent analytical modules: Energy Consumption Profiling, Vehicle Fleet Composition Analysis, Traffic Density Mapping, Random Event Disruption Evaluation, Economic Impact Assessment, and Weather Effects Evaluation. The framework employs rigorous data preprocessing and predictive extensions using XG-Boost/Light-GBM models that forecast energy consumption and congestion levels. Experimental results demonstrate that the integrated approach significantly outperforms single-perspective analyses, achieving up to 98% accuracy in identifying high-stress periods and revealing actionable patterns such as 15–18% energy spikes during rainy weekday rush hours. Urban-Flow AI serves as a practical decision-support tool for urban planners, transportation authorities, and policymakers aligned with UN SDGs 11 and 13.},
        keywords = {sustainable urban mobility; multi-module analytics; data visualization; predictive modeling; traffic congestion; energy efficiency; smart cities; decision support system1},
        month = {May},
        }