Chapter
Aug 12, 2020
High Resolution Speed Estimation for Large-Scale Freeway Based on Data Fusion Technology
Authors: Yanjie Gui [email protected], Fan Ding [email protected], Hanxuan Dong [email protected], Jiankun Peng [email protected], and Huachun Tan [email protected]Author Affiliations
Publication: CICTP 2020
ABSTRACT
Obtaining high resolution traffic states of large-scale freeway is always a significant topic for both transportation engineers and researchers. This paper presents a machine learning based high resolution speed estimation method for large-scale freeway using two data sources. Two low resolution heterogeneous traffic data are collected from microscopic simulations with different error distributions. A neural network based model is implemented fusing the two data sources and improving both time and space resolution of traffic estimations. The validation results and the sensitivity analysis indicate that the proposed method is feasible and suitable for large-scale freeway speed estimation. The performance of the model is acceptable, and the model could indeed improve both time and space resolutions of the estimations.
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Published In
CICTP 2020
Pages: 654 - 664
Copyright
© 2020 American Society of Civil Engineers.
History
Published online: Aug 12, 2020
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Affiliations
1Joint Research Institute on Internet of Mobility, Southeast Univ. and Univ. of Wisconsin-Madison, School of Transportation, No. 2 Southeast University Rd., Nanjing 211189, PR China. Email: [email protected]
2Joint Research Institute on Internet of Mobility, Southeast Univ. and Univ. of Wisconsin-Madison, School of Transportation, No. 2 Southeast University Rd., Nanjing 211189, PR China. Email: [email protected]
3Joint Research Institute on Internet of Mobility, Southeast Univ. and Univ. of Wisconsin-Madison, School of Transportation, No. 2 Southeast University Rd., Nanjing 211189, PR China. Email: [email protected]
4Joint Research Institute on Internet of Mobility, Southeast Univ. and Univ. of Wisconsin-Madison, School of Transportation, No. 2 Southeast University Rd., Nanjing 211189, PR China. Email: [email protected]
5Joint Research Institute on Internet of Mobility, Southeast Univ. and Univ. of Wisconsin-Madison, School of Transportation, No. 2 Southeast University Rd., Nanjing 211189, PR China. Email: [email protected]
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ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.
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