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A V2I communication-based pipeline model for adaptive urban traffic light scheduling

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Abstract

Adaptive traffic light scheduling based on realtime traffic information processing has proven effective for urban traffic congestion management. However, fine-grained information regarding individual vehicles is difficult to acquire through traditional data collection techniques and its accuracy cannot be guaranteed because of congestion and harsh environments. In this study, we first build a pipeline model based on vehicle-to-infrastructure communication, which is a salient technique in vehicular adhoc networks. This model enables the acquisition of fine-grained and accurate traffic information in real time via message exchange between vehicles and roadside units. We then propose an intelligent traffic light scheduling method (ITLM) based on a “demand assignment” principle by considering the types and turning intentions of vehicles. In the context of this principle, a signal phase with more vehicles will be assigned a longer green time. Furthermore, a green-way traffic light scheduling method (GTLM) is investigated for special vehicles (e.g., ambulances and fire engines) in emergency scenarios. Signal states will be adjusted or maintained by the traffic light control system to keep special vehicles moving along smoothly. Comparative experiments demonstrate that the ITLM reduces average wait time by 34%–78% and average stop frequency by 12%–34% in the context of traffic management. The GTLM reduces travel time by 22%–44% and 30%–55% under two types of traffic conditions and achieves optimal performance in congested scenarios.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant Nos. 61472287, 61572370), and the Science and Technology Support Program of Hubei Province (2015CFA068).

Author information

Authors and Affiliations

  1. State Key Laboratory of Software Engineering, Wuhan University, Wuhan, 430072, China

    Libing Wu

  2. Computer School, Wuhan University, Wuhan, 430072, China

    Libing Wu & Lei Nie

  3. School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430065, China

    Lei Nie

  4. Department of Electrical and Computer Engineering, North Dakota State University, Fargo, USA

    Samee U. Khan

  5. COMSATS Institute of Information Technology, Islamabad, Pakistan

    Osman Khalid

  6. School of Computer Science, University of Windsor, Windsor, Ontario, Canada

    Dan Wu

Authors
  1. Libing Wu
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  2. Lei Nie
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  3. Samee U. Khan
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  4. Osman Khalid
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  5. Dan Wu
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Corresponding author

Correspondence to Libing Wu.

Additional information

Libing Wu received his PhD degree from Wuhan University, China in 2006. In 2011, he was a visiting scholar at the Laboratory for Advanced Networking, University of Kentucky, Lexington, KY, USA. He is currently a professor at Computer School of Wuhan University, China. His research interests include vehicular ad hoc networks and distributed computing.

Lei Nie received his PhD degree from Wuhan University, China in 2017. He is currently a lecturer in the School of Computer Science and Technology, Wuhan University of Science and Technology, China. His main research interests include vehicular ad hoc networks and wireless communication.

Samee U. Khan received his BS degree in 1999 from Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi, Pakistan, and his PhD degree in 2007 from the University of Texas, Arlington, TX, USA. He is currently an associate professor of Electrical and Computer Engineering at the North Dakota State University, Fargo, ND, USA. His research interests include optimization, robustness, and security of systems. He is on the editorial boards of leading journals, such as IEEE Access, IEEE Communications Surveys and Tutorials, IET Wireless Sensor Systems, Scalable Computing, IET Cyber-Physical Systems and IEEE IT Pro. He is an ACM Distinguished Speaker, an IEEE Distinguished Lecturer, a Fellow of the Institution of Engineering and Technology (IET, formerly IEE), and a Fellow of the British Computer Society (BCS).

Osman Khalid received his PhD degree in electrical and computer engineering from North Dakota State University, Fargo. He is currently an assistant professor of computer sciences at the COMSATS Institute of Information Technology, Abbottabad, Pakistan. His research interests include opportunistic networks, recommendation systems, and trust and reputation systems.

Dan Wu received his PhD degree from University of Regina, Canada in 2003. He is currently an associate professor at School of Computer Science, University of Windsor. His research interests include uncertainty reasoning in artificial intelligence and mobile robotics.

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Wu, L., Nie, L., Khan, S.U. et al. A V2I communication-based pipeline model for adaptive urban traffic light scheduling. Front. Comput. Sci. 13, 929–942 (2019). https://doi.org/10.1007/s11704-017-7043-3

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  • DOI: https://doi.org/10.1007/s11704-017-7043-3

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