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Low-Carbon Emission Driven Traffic Speed Optimization for Internet of Vehicles

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Edge Computing and IoT: Systems, Management and Security (ICECI 2020)

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Abstract

Climate change has become a worldwide concern. Reducing CO2 emission is a major challenge for road transportation sector and is of critical importance. This paper, after studying and analyzing the influence of speed on vehicle CO2 emission, proposes a recommended speed calculation scheme based on IoV to obtain vehicle speed and traffic signal phase information. In the recommended speed scenario, the vehicle is informed of the traffic phase information before arriving at the intersection and can set and optimize the current speed. This paper analyzes the three different status of traffic lights and studies the speed that should be adopted in each status. Under the proposed scheme, the recommended speed helps the driver to reach the destination with higher driving efficiency. The average wait time at red traffic lights is shorter than at speeds that are not recommended, resulting in reduced total travel time, higher uninterrupted pass rates, and decreased vehicle fuel consumption and CO2 emissions.

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Acknowledgement

This work was supported by the National Social Sciences Foundation of China (Grants No. 19CGL043).

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Authors and Affiliations

  1. Business College, Central South University of Forestry and Technology, Changsha, 410004, China

    Wenjie Chen & Zou Siming

  2. College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China

    Zhende Xiao

Authors
  1. Wenjie Chen
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  2. Zhende Xiao
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  3. Zou Siming
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Corresponding author

Correspondence to Wenjie Chen .

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Editors and Affiliations

  1. Hunan University, Hunan, China

    Hongbo Jiang

  2. Old Dominion University, Norfolk, VA, USA

    Hongyi Wu

  3. Hunan University, Changsha, China

    Fanzi Zeng

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Chen, W., Xiao, Z., Siming, Z. (2021). Low-Carbon Emission Driven Traffic Speed Optimization for Internet of Vehicles. In: Jiang, H., Wu, H., Zeng, F. (eds) Edge Computing and IoT: Systems, Management and Security. ICECI 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 368. Springer, Cham. https://doi.org/10.1007/978-3-030-73429-9_3

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  • DOI: https://doi.org/10.1007/978-3-030-73429-9_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-73428-2

  • Online ISBN: 978-3-030-73429-9

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