This sessions presents various real-time applications of machine learning models and tools. Applications include short-term forecasting of traffic speeds, prediction of traffic status in spatiotemporal dimension, estimation of vehicle energy, and assessment of pavement condition.
Title | Presentation Number |
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Deep Learning and UAV Based Solution to Real Time Pavement Condition Assessment
MURAD AL QURISHEE, Tennessee Department of Transportation
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WEIDONG WU, University of Tennessee, Chattanooga Babatunde Atolagbe, Maryland State Highway Administration Joseph Owino, UTC Ignatius Fomunung, University of Tennessee, Chattanooga Mbakisya Onyango, University of Tennessee, Chattanooga |
TRBAM-21-00056 |
A Real-time Spatiotemporal Prediction and Imputation of Traffic Status Based on LSTM and Graph Laplacian Regularized Matrix Factorization
Jin-Ming Yang (yangjm67@sjtu.edu.cn), Shanghai Jiao Tong University
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Zhong-Ren Peng, University of Florida Lei Lin, University of Rochester |
TRBAM-21-00978 |
A Meta-Learner Ensemble Framework for Real-Time Short-Term Traffic Speed Forecasting
Divyakant Tahlyan, Northwestern University
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Eunhye Kim, Northwestern University Hani Mahmassani, Northwestern University |
TRBAM-21-02198 |
Real-Time Highly Resolved Spatial-Temporal Vehicle Energy Estimation Using Machine Learning and Probe Data
Joseph Severino, National Renewable Energy Laboratory (NREL)
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Yi Hou, National Renewable Energy Laboratory (NREL) Ambarish Nag, National Renewable Energy Laboratory (NREL) Jacob Holden, National Renewable Energy Laboratory (NREL) Lei Zhu, University of North Carolina, Charlotte Juliette Ugirurmurera, National Renewable Energy Laboratory (NREL) Stanley Young, National Renewable Energy Laboratory (NREL) Wesley Jones, National Renewable Energy Laboratory (NREL) Jibonananda Sanyal, Oak Ridge National Laboratory |
TRBAM-21-03287 |
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