Chapter
Jun 29, 2016
Real-Time Traffic Speed Estimation with Adaptive Cruise Control Vehicles and Manual Vehicles in a Mixed Environment
Authors: Zhiwei Li [email protected], Jian Zhang [email protected], Haiyan Gu [email protected], and Shanglu He [email protected]Author Affiliations
Publication: CICTP 2016
Abstract
With the development of adaptive cruise control (ACC) vehicles, the traffic on freeways will be a mixture of ACC vehicles and traditional manual vehicles. It is a new challenge for freeway traffic state estimation. This paper proposes a neural network-based method to estimate the traffic speed under the mixed traffic. Three different neural network models are investigated using the simulation data, which is programmed by MATLAB. The results indicate that the neural network model with three input neurons (average speed of ACC vehicles, speeds from the microwave detector, and percentage of ACC vehicles) has a significant performance. Besides the fusion of measurements from the microwave detectors and ACC vehicles could improve the overall estimation accuracy, especially, the penetrate rate of ACC vehicles which is below 35%.
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Published In
CICTP 2016
Pages: 51 - 61
Copyright
© 2016 American Society of Civil Engineers.
History
Published online: Jun 29, 2016
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Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast Univ.; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies; Jiangsu Province Collaborative Innovation Center for Technology and Application of Internet of Things; Research Center for Internet of Mobility, Si Pai Lou #2, Nanjing, Jiangsu 210096, China. E-mail: [email protected]
Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast Univ.; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies; Jiangsu Province Collaborative Innovation Center for Technology and Application of Internet of Things; Research Center for Internet of Mobility, Si Pai Lou #2, Nanjing, Jiangsu 210096, China. E-mail: [email protected]
Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast Univ.; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies; Jiangsu Province Collaborative Innovation Center for Technology and Application of Internet of Things; Research Center for Internet of Mobility, Si Pai Lou #2, Nanjing, Jiangsu 210096, China. E-mail: [email protected]
Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast Univ.; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies; Jiangsu Province Collaborative Innovation Center for Technology and Application of Internet of Things; Research Center for Internet of Mobility, Si Pai Lou #2, Nanjing, Jiangsu 210096, China. E-mail: [email protected]
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