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Real-Time Traffic Flow Management Based on Inter-Object Communication: a Case Study at Intersection

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Abstract

Intersections become very congested when traffic volumes are high, creating inefficiency that results in user delay and frustration. There have been many approaches which focus on optimization signal of Traffic Light System and Vehicle Trajectory Analysis to improve traffic flow at intersection. However, to implement those approaches into reality become a challenges since real-time problem. In this study, inspired by recent advanced vehicle technologies, we propose an approach for traffic flow management at intersection. In particular, with the exploding at an enormous rate of Internet of Things (IoT), the connected object has been the most visible and familiar application. By this way, based on connected object, we design a model which communicating among objects to improve traffic flow at intersection with real time problem. Moreover, traffic congestion is also taken into consideration in case of high traffic volume. The simulation shows the potential results comparing with the existing traffic management system.

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Notes

  1. Telefonica, “ Car Industry Report ”,Tech.rep.,2013

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Acknowledgments

This research was supported by Chung-Ang University Research Grants in 2016.

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Authors and Affiliations

  1. Department of Computer Engineering, Chung-Ang University, Seoul, Korea

    Khac-Hoai Nam Bui

  2. Department of Computer Science, Universidad Autonoma de Madrid, Madrid, Spain

    David Camacho

  3. Computer Science Department, Chung-Ang University, Seoul, Korea

    Jai E. Jung

Authors
  1. Khac-Hoai Nam Bui
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  2. David Camacho
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  3. Jai E. Jung
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Corresponding author

Correspondence to Jai E. Jung.

Additional information

This paper is significantly revised from an earlier version presented at The 7th International Conference on Ambient Intelligence (ISAmI’2016) in June 2016.

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Bui, KH.N., Camacho, D. & Jung, J.E. Real-Time Traffic Flow Management Based on Inter-Object Communication: a Case Study at Intersection. Mobile Netw Appl 22, 613–624 (2017). https://doi.org/10.1007/s11036-016-0800-y

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