TEJAS Journal of Technologies and Humanitarian Science

Shahnawaz
Research scholar , Department of Computer Science & Engineering , KIPM College of Engineering & Technology , Gorakhpur , India

Author

Anshuman Singh
Research scholar , Department of Computer Science & Engineering , KIPM College of Engineering & Technology , Gorakhpur , India

Author

Ankita Gupta
Research scholar , Department of Computer Science & Engineering , KIPM College of Engineering & Technology , Gorakhpur , India

Author

Ranjeet Kumar Dubey
Assistant Professor, Department of Computer Science & Engineering , KIPM College of Engineering & Technology , Gorakhpur , India

Author

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Abstract:

RideSight ML is an integrated machine-learning platform designed to analyze, predict, and optimize mobility patterns across urban and regional transportation networks. Leveraging multimodal data streams—including GPS traces, public transit feeds, traffic sensors, micromobility data, and contextual variables such as weather and events—RideSight ML provides high-resolution insights into traveler demand, network performance, and system bottlenecks. The platform employs advanced statistical learning, spatiotemporal forecasting models, and graph-based neural networks to capture dynamic movement behaviors and infer latent mobility structures. RideSight ML: Mobility Analytics & Forecasting is an advanced machine-learning framework designed to transform raw transportation data into actionable mobility intelligence. Leveraging multimodal data sources—including GPS trajectories, transit schedules, shared-mobility feeds, traffic sensors, and contextual signals such as weather and events—the system employs deep learning architectures and probabilistic modeling to uncover patterns in urban movement. Core components include real-time demand prediction, dynamic travel-time estimation, anomaly detection for network disruptions, and passenger flow forecasting across modes.

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