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Real-Time Smart Traffic Analysis Employing a Dual Approach Based on AI

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Proceedings of International Conference on Recent Trends in Computing

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 600))

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Abstract

Sensor data, which is also accurate, is used in the bulk of studies on traffic-related data. The volume of this data, however, is insufficient to cover the majority of the road network due to the high cost. To get a complete and accurate range of data, image processing-based solutions with higher compatibility and ease of maintenance, as well as sensors, are necessary (Rakesh et al., Int J Sci Technol Res 8(12) (2019)). Free-flowing traffic is harder to detect and manage than dedicated lanes, and therefore necessitates more exact forecasting. A traffic analysis system based on the random forest algorithm is presented in this study, which predicts traffic congestion on a given road and notifies users well in advance.

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Author information

Authors and Affiliations

  1. Maharishi Markandeshwer (Deemed to Be) University, Mullana, India

    Neera Batra & Sonali Goyal

Authors
  1. Neera Batra
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  2. Sonali Goyal
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Corresponding author

Correspondence to Neera Batra .

Editor information

Editors and Affiliations

  1. SRM Institute of Science and Technology, Ghaziabad, Uttar Pradesh, India

    Rajendra Prasad Mahapatra

  2. Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, India

    Sateesh K. Peddoju

  3. Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Sudip Roy

  4. SRM Institute of Science and Technology, Ghaziabad, Uttar Pradesh, India

    Pritee Parwekar

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Batra, N., Goyal, S. (2023). Real-Time Smart Traffic Analysis Employing a Dual Approach Based on AI. In: Mahapatra, R.P., Peddoju, S.K., Roy, S., Parwekar, P. (eds) Proceedings of International Conference on Recent Trends in Computing. Lecture Notes in Networks and Systems, vol 600. Springer, Singapore. https://doi.org/10.1007/978-981-19-8825-7_61

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