Authors: Ayush Vishwakarma, Yashi Verma
Abstract: Accurate estimation of travel time is no longer a luxury but a necessity in modern navigation systems, directly impacting user trust and urban transportation efficiency. As cities grow more complex and dynamic, conventional prediction models struggle to adapt to real-time changes. This paper explores the transformative role of big data and artificial intelligence (AI) in refining Estimated Time of Arrival (ETA) predictions, with a focus on Google Maps. Leveraging massive datasets—including GPS trajectories, historical travel data, real-time traffic flows, and userreported incidents—Google Maps employs advanced machine learning algorithms to make adaptive and reliable ETA forecasts [3][4][8][9]. This study investigates how these AI models interpret multilayered traffic data to generate predictions, even under volatile traffic conditions. It further examines how deep learning architectures and neural networks detect patterns, anomalies, and geographic variations in travel behaviours [1][2][19]. A time-based graphical analysis illustrates the improvements in ETA prediction accuracy from 2017 to 2025, emphasizing the system’s continual evolution. Additionally, the paper breaks down the core data sources that fuel this predictive engine, offering insights into the structure and effectiveness of Google Maps’ data pipeline [5][6][7]. As part of this research, we also propose a novel real-time user feedback mechanism designed to enhance live traffic prediction by incorporating human intelligence in the loop. The system enables commuters to quickly report congestion, blockages, or discrepancies, providing hyper-local input that can improve ETA accuracy, especially in under-reported areas.
DOI: http://doi.org/
