Geospatial-Enabled Machine Learning Models for Traffic Flow Prediction in Smart Cities

  • Authors

    • Rohan Malhotra Co-founder, Good Capital, India Author

    DOI:

    https://doi.org/10.67228/30715636/IJETMR-2019PII5D6X

    Published 08-05-2019

  • Traffic Flow Prediction, Geospatial Data, Machine Learning, Smart Cities, Spatial-Temporal Modeling, Urban Mobility

    Issue

    Vol. 2 No. 2 (2019)

    Section

    Articles

    How to Cite

    [1]
    R. Malhotra, “Geospatial-Enabled Machine Learning Models for Traffic Flow Prediction in Smart Cities”, IJETMR, vol. 2, no. 2, pp. 01–07, Aug. 2019, doi: 10.67228/30715636/IJETMR-2019PII5D6X.

  • Abstract

    In the era of smart cities, efficient traffic management is paramount to ensure smooth urban mobility and reduce congestion. Traditional traffic prediction models often overlook the spatial dependencies inherent in traffic flow data. This paper explores the integration of geospatial data with machine learning techniques to enhance traffic flow prediction accuracy. By leveraging geospatial information, such as road networks and traffic sensor locations, alongside advanced machine learning algorithms, we propose models that capture both spatial and temporal traffic patterns. The study demonstrates that incorporating geospatial features significantly improves prediction performance compared to conventional methods. Our findings offer valuable insights for urban planners and traffic management authorities aiming to implement data-driven solutions for smart city traffic systems.

  • References

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