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An IoT-Based Congestion Control Framework for Intelligent Traffic Management System

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Advances in Artificial Intelligence and Data Engineering (AIDE 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1133))

Abstract

The concept of smart city helps to improve the quality of urban life of the citizens while keeping in mind the environmental impacts. Smart and sustainable transportation system is one of the major contributors in order to make the city smart. Major cities around the world face enormous vehicular growth due to socioeconomic growth and rural to urban migration of the people. These results in high traffic congestion on road, road accidents, delay and have an adverse environmental impact, thus effecting smooth mobility of the citizens. Hence, traffic management authorities face difficulties to manage and reduce traffic congestion, road accidents and air pollution. In order to overcome the above-mentioned challenges, this paper proposes a framework for managing road traffic congestion in intelligent traffic management system which utilizes the available infrastructures and resources in an optimum way. The proposed framework comprises of four different modules, namely data collection module, data storage module, data processing module and business application module.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Narula Institute of Technology, Kolkata, 700109, India

    Md. Ashifuddin Mondal

  2. Department of Computer Science and Engineering, Aliah University, New Town, Kolkata, 700160, India

    Zeenat Rehena

Authors
  1. Md. Ashifuddin Mondal
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  2. Zeenat Rehena
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Corresponding author

Correspondence to Md. Ashifuddin Mondal .

Editor information

Editors and Affiliations

  1. NMAM Institute of Technology, Udupi, India

    Niranjan N. Chiplunkar

  2. Ritsumeikan University, Shiga, Japan

    Takanori Fukao

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Mondal, M.A., Rehena, Z. (2021). An IoT-Based Congestion Control Framework for Intelligent Traffic Management System. In: Chiplunkar, N.N., Fukao, T. (eds) Advances in Artificial Intelligence and Data Engineering. AIDE 2019. Advances in Intelligent Systems and Computing, vol 1133. Springer, Singapore. https://doi.org/10.1007/978-981-15-3514-7_96

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