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Traffic Flow Prediction Using Regression and Deep Learning Approach

In: New Trends in Computational Vision and Bio-inspired Computing

Author

Listed:
  • Savita Lonare

    (GITAM, Department of CSE)

  • R. Bhramaramba

    (GITAM, Department of CSE)

Abstract

Accurate prediction of traffic makes it easy to make decisions of travelling route, travelling schedule, travel vehicles choice for a commuter. The surveillance systems, GPS system installed on road way are the abundant source of traffic data. This huge amount of traffic data and increased computing power definitely motivates researchers to analyze the data to solve the road traffic and transportation problems. Deep learning always proved to be a good solution for prediction problems such as audio classification, signal processing, image classification and Network traffic prediction. This research shows that Long short term memory network can be used to design a traffic forecast model. Many researchers have proved that deep sigmoidal networks could be trained to produce good results for many tasks such as audio classification, signal processing, image classification and Network traffic prediction. In this work comparison of LSTM with Linear Regression, Logistics Regression and ARIMA was done.

Suggested Citation

  • Savita Lonare & R. Bhramaramba, 2020. "Traffic Flow Prediction Using Regression and Deep Learning Approach," Springer Books, in: S. Smys & Abdullah M. Iliyasu & Robert Bestak & Fuqian Shi (ed.), New Trends in Computational Vision and Bio-inspired Computing, pages 641-648, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-41862-5_63
    DOI: 10.1007/978-3-030-41862-5_63
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