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A Data-Agnostic Hybrid SARIMA–LSTM framework for Urban Traffic Monitoring in Smart cities

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Author Affiliations

  • 1Department of Engineering Sciences and Humanity, Chameli Devi Group of Institutions, Indore 452020, MP, India
  • 2Department of Applied Mathematics and Computational Science, SGSITS, Indore 452003, MP, India
  • 3Department of Engineering Sciences and Humanity, Chameli Devi Group of Institutions, Indore 452020, MP, India

Res. J. Engineering Sci., Volume 15, Issue (3), Pages 10-14, September,26 (2026)

Abstract

The monitoring of urban traffic is critical for effective management in smart city and transport systems. Quite often, existing forecasting approaches tend to be heavily dependent on bulky location-specific data sets, which results in their applicability being questionable under conditions of limited data. In this paper, we suggest developing a novel SARIMA-LSTM approach that combines the principles of statistical time-series analysis with deep learning-based correction of residuals. In the first place, the SARIMA algorithm is introduced in order to guarantee interpretability and statistically proven characteristics of our model due to the identification of linear and seasonal trends. At the same time, the LSTM network is applied to imitate the dynamic non-linearities in residuals in order to improve short-term forecasts. The theoretical development of the approach, its design and experimental verification are provided in the paper. Moreover, the suggested model structure offers an environmentally friendly opportunity for monitoring of urban traffic and facilitates the integration with future spatial-temporal and real-time intelligent transport systems.

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