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      A Simulation System and Speed Guidance Algorithms for Intersection Traffic Control Using Connected Vehicle Technology

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          In the connected vehicle environment, real-time vehicle-state data can be obtained through vehicle-to-infrastructure communication, and the prediction accuracy of urban traffic conditions can significantly increase. This study uses the C++/Qt programming language and framework to build a simulation platform. A two-way six-lane intersection is set up on the simulation platform. In addition, two speed guidance algorithms based on optimizing the travel time of a single vehicle or multiple vehicles are proposed. The goal of optimization is to minimize the travel time, with common indicators such as average delay of vehicles, average number of stops, and average stop time chosen as indexes of traffic efficiency. When the traffic flow is not saturated, compared with the case of no speed guidance, single-vehicle speed guidance can improve the traffic efficiency by 20%, whereas multi-vehicle speed guidance can improve the traffic efficiency by 50%. When the traffic flow is saturated, the speed guidance algorithms show outstanding performance. The effect of speed guidance gradually enhances with increasing penetration rate, and the most obvious gains are obtained when the penetration rate increases from 10% to 40%. Thus, this study has shown that speed guidance in the connected vehicle environment can significantly improve the traffic efficiency of intersections, and the multi-vehicle speed guidance strategy is more effective than the single-vehicle speed guidance strategy.

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          Author and article information

          Tsinghua Science and Technology
          Tsinghua University Press (Xueyan Building, Tsinghua University, Beijing 100084, China )
          05 April 2019
          : 24
          : 2
          : 160-170
          ∙ Shuai Liu, Weitong Zhang, Shuo Feng, Xin Pei, and Danya Yao are with Department of Automation, Tsinghua University National Laboratory for Information Science and Technology (TNList), Tsinghua University, Beijing 100084, China. E-mail: liushuaicc11326@ 123456126.com ; zwt15@ 123456mails.tsinghua.edu.cn ; fengshuo10@ 123456163.com ; yaody@ 123456tsinghua.edu.cn .
          ∙ Xiaojun Wu is with Graduate School of Tsinghua University, Beijing 100084, China. E-mail: wuxiaojun@ 123456tsinghua.edu.cn .
          Author notes
          * To whom correspondence should be addressed. E-mail: peixin@ 123456tsinghua.edu.cn .

          Shuai Liu is currently a PhD candidate of Department of Automation, Tsinghua University, China. He received BEng degree from Tsinghua University, China in 2011. He has participated in several research projects granted from MOST, NSFC, and etc. His research interests include intelligent transportation system (ITS), intelligent vehicle-infrastructure cooperation systems, connected vehicles, and cooperative driving.

          Weitong Zhang is an undergraduate student in Department of Automation, Tsinghua University, China. He has worked on several research projects on intelligent transportation systems, especially intelligent vehicle-infrastructure cooperation systems. His active research interests include artificial intelligence, machine learning, optimization, computer vision, and computer graphics.

          Danya Yao received BEng degree in 1988, MEng in 1990, and PhD degree in 1994 from Tsinghua University, China. He is a professor in control science and engineering at Tsinghua University with his current research interests focusing on intelligent transportation systems. His active areas include intelligent vehicle-infrastructure cooperative systems, connected vehicles, cooperative driving, high accuracy positioning, advanced driver assistant system, cooperative driver assistant system, traffic behaviour analysis and modeling, advanced detection and measurement, cooperative traffic control and optimization, and distributed sensor networks.

          Xin Pei received the BS and MS degrees from Tsinghua University, China, in 2005 and 2007, respectively, and the PhD degree from the University of Hong Kong in 2011. She is currently an Associate Professor with Department of Automation, Tsinghua University. She serves as a journal reviewer for several international journals and conference including Accident Analysis and Prevention, Transportmetrica, and ITSC conference, etc. Her current research interests include road safety evaluation and driving behavior analysis.

          Xiaojun Wu is now a lecturer in Tsinghua University, China. Her PhD in management science and engineering was awarded from Tsinghua University, China in 2016, with a doctorial period partially spent at Ohio State University, USA. Her research interests include interface design, alarm systems in nuclear power plants, cognitive engineering, and driving behavior analysis.

          Shuo Feng received the BEng degree from Tsinghua University, China, in 2014. He is currently a PhD candidate in Department of Automation, Tsinghua University, China. He also researches as a joint PhD student in civil and environmental engineering in University of Michigan, Ann Arbor. His current research interests include optimal control, connected and automated vehicle evaluation, and transportation data analysis.



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