Traffic volume forecasting based on radial basis function neural network with the consideration of traffic flows at the adjacent intersections |
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| Affiliation: | 1. Institute of Transportation, Inner Mongolia University, Hohhot 010070, PR China;2. MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, PR China;3. Polytechnic Institute, New York University, NY 11201, USA;1. Civil & Environmental Engineering, Transportation Research Institute at Old Dominion University (ODU), 132 Kufman Hall, Norfolk, VA 23529, United States;2. Director of Transportation Research Institute at ODU, Civil & Environmental Engineering, Transportation Research Institute at Old Dominion University (ODU), 135 Kufman Hall, Norfolk, VA 23529, United States;1. College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;2. Shanghai Key Laboratory of Financial Information Technology, Shanghai University of Finance and Economics, Shanghai 200433, China;1. Intelligent Transportation System Research Center, Southeast University, Si Pai Lou #2, Nanjing 210096, PR China;2. Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC 27695, USA;1. School of Transportation Science and Engineering, Beijing Key Laboratory for Cooperative Vehicle Infrastructure, Systems, and Safety Control, Beihang University, Beijing 100191, China;2. Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, SiPaiLou #2, Nanjing 210096, China;3. Department of Science and Technology, Beijing Traffic Management Bureau, Beijing 100037, China;4. Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195-2700, United States;1. Nextrans Center, Purdue University, West Lafayette, IN 47906, USA;2. School of Transportation, Southeast University, Nanjing, China |
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| Abstract: | The forecasting of short-term traffic flow is one of the key issues in the field of dynamic traffic control and management. Because of the uncertainty and nonlinearity, short-term traffic flow forecasting could be a challenging task. Artificial Neural Network (ANN) could be a good solution to this issue as it is possible to obtain a higher forecasting accuracy within relatively short time through this tool. Traditional methods for traffic flow forecasting generally based on a separated single point. However, it is found that traffic flows from adjacent intersections show a similar trend. It indicates that the vehicle accumulation and dissipation influence the traffic volumes of the adjacent intersections. This paper presents a novel method, which considers the travel flows of the adjacent intersections when forecasting the one of the middle. Computational experiments show that the proposed model is both effective and practical. |
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| Keywords: | Traffic volume Forecasting method Data mining Neural networks Flocking phenomena Missing data |
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