Real-time AI-driven trend analytics for smart city digital services using streaming data.

Authors

Zhuldyz Kalpeyeva, Abdul Razaque, Raissa Uskenbayeva, Aliya Beishenaly, Venera Elle, Aizhan Kassymova

Published in

Frontiers in big data. Volume 9. Pages 1811835. Epub Aug 14, 2026.

Abstract

Real-time data analysis plays an important role in the operation of digital urban systems, where the behavior of residents and the load on services can change over short periods of time. At the same time, traditional analytical approaches based on batch data processing often do not allow timely detection of such changes, which leads to delayed and not always accurate management decisions. This paper introduces an artificial intelligence-driven smart city system (AISSC) for real-time data trend analysis. The proposed AISSC framework processes streaming data in a smart city digital environment. The proposed approach encompasses real-time feature generation and statistical techniques for identifying significant changes. The proposed AISSC solution is executed on the Python platform. The results demonstrate that the proposed AISSC solution achieves a directional accuracy of 98.6% for trend prediction, together with strong numerical forecasting performance with a MAPE of 6.3% and a WMAPE of 7.8%. The framework detects statistically significant deviations within 2.4 s at the sliding-window level while maintaining an end-to-end system update cycle of approximately 5 min. These results demonstrate the capability of the proposed framework to support reliable real-time trend analysis and short-term forecasting for smart city decision-making. This shows that the framework can reliably identify trends and accurately estimate demand for real-time smart city decision-making. The results demonstrate consistent performance improvements compared to representative baseline methods under identical streaming and computational constraints. The proposed AISSC facilitates real-time observation of urban dynamics and enhances short-term forecasting accuracy for informed decision-making in smart city management systems.

PMID:
42666299
Bibliographic data and abstract were imported from PubMed on 29 Aug 2026.

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