Machine Learning Framework for Improving Accuracy of Probe Speed Data-Reference-Cited by-同舟云学术

Machine Learning Framework for Improving Accuracy of Probe Speed Data

Published:2021-06 Issue:2 Volume:7 Page:04021006
ISSN:2376-7642
Container-title:ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
language:en
Short-container-title:ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng.

Author:

Phuong Uong Lan¹,Adu-Gyamfi Yaw²^ORCID,Zhao Mo³^ORCID

Affiliation:

1. Dept. of Civil and Environment Engineering, Univ. of Missouri–Columbia, E1511 Lafferre Hall, Columbia, MO 65211.

2. Assistant Professor, Dept. of Civil and Environment Engineering, Univ. of Missouri–Columbia, E1511 Lafferre Hall, Columbia, MO 65211 (corresponding author). ORCID: .

3. Dept. of Civil and Environment Engineering, Virginia Transportation Research Council, 530 Edgemont Rd., Charlottesville, VA 22903. ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Subject

Safety, Risk, Reliability and Quality,Building and Construction,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/AJRUA6.0001120

Reference34 articles.

1. Abadi M. et al. 2016. “Tensorflow: A system for large-scale machine learning.” In Proc. Symp. on Operating Systems Design and Implementation 265–283. Berkeley CA: The Advanced Computing Systems Association.

2. Adu-Gyamf Y. O. 2019. “LSTM probe speed prediction.” Accessed August 8 2012. https://github.com/TITAN-lab/LSTM-PredictProbeSpeed.

3. Critical Analysis of Different Hilbert-Huang Algorithms for Pavement Profile Evaluation

4. Framework for Evaluating the Reliability of Wide-Area Probe Data

5. Adu-Gyamfi Y. O. and M. Zhao. 2018. “Traffic speed prediction for urban arterial roads using deep neural networks.” In Proc. Int. Conf. on Transportation and Development. Reston VA: ASCE. https://doi.org/10.1061/9780784481547.009.

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