Empirical study of the effects of physics-guided machine learning on freeway traffic flow modelling: model comparisons using field data

Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems

(2022) Jared Willard et al.   ACM COMPUTING SURVEYS

Graph neural network for traffic forecasting: A survey

(2022) Weiwei Jiang et al.   EXPERT SYSTEMS WITH APPLICATIONS

Macroscopic traffic flow modeling with physics regularized Gaussian process: A new insight into machine learning applications in transportation

(2021) Yun Yuan et al.   TRANSPORTATION RESEARCH PART B-METHODOLOGICAL

A Physics-Informed Deep Learning Paradigm for Traffic State and Fundamental Diagram Estimation

(2021) Rongye Shi et al.   IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS

Obstacle segmentation based on the wave equation and deep learning

(2020) Adar Kahana et al.   JOURNAL OF COMPUTATIONAL PHYSICS

GE-GAN: A novel deep learning framework for road traffic state estimation

(2020) Dongwei Xu et al.   TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES

A Hybrid Machine Learning Approach for Freeway Traffic Speed Estimation

(2020) Zhao Zhang et al.   TRANSPORTATION RESEARCH RECORD

Freeway Traffic Speed Estimation by Regression Machine-Learning Techniques Using Probe Vehicle and Sensor Detector Data

(2020) Zhao Zhang et al.   Journal of Transportation Engineering Part A-Systems

Traffic speed prediction for urban transportation network: A path based deep learning approach

(2019) Jiawei Wang et al.   TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES

Bayesian Particle Tracking of Traffic Flows

(2018) Nicholas Polson et al.   IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS

Speed estimation of traffic flow using multiple kernel support vector regression

(2018) Jianli Xiao et al.   PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS

Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics

(2018) Linfeng Zhang et al.   PHYSICAL REVIEW LETTERS

A hybrid deep learning based traffic flow prediction method and its understanding

(2018) Yuankai Wu et al.   TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES

Deep learning for short-term traffic flow prediction

(2017) Nicholas G. Polson et al.   TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES

Data-Driven Imputation Method for Traffic Data in Sectional Units of Road Links

(2016) Sehyun Tak et al.   IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS

An efficient realization of deep learning for traffic data imputation

(2016) Yanjie Duan et al.   TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES

Hybrid Approach for Short-Term Traffic State and Travel Time Prediction on Highways

(2016) Andreas Allström et al.   TRANSPORTATION RESEARCH RECORD

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(2016)   JOURNAL OF ADVANCED TRANSPORTATION

INTEGRATION OF EXPONENTIAL SMOOTHING WITH STATE SPACE FORMULATION FOR BUS TRAVEL TIME AND ARRIVAL TIME PREDICTION

(2015) Selvaraj Vasantha Kumar et al.   Transport

A hybrid approach to integrate fuzzy C-means based imputation method with genetic algorithm for missing traffic volume data estimation

(2015) Jinjun Tang et al.   TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES

Long short-term memory neural network for traffic speed prediction using remote microwave sensor data

(2015) Xiaolei Ma et al.   TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES

INTEGRATION OF EXPONENTIAL SMOOTHING WITH STATE SPACE FORMULATION FOR BUS TRAVEL TIME AND ARRIVAL TIME PREDICTION

(2015) Selvaraj Vasantha Kumar et al.   Transport

Spatiotemporal Patterns in Large-Scale Traffic Speed Prediction

(2014) Muhammad Tayyab Asif et al.   IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS

Robust Missing Traffic Flow Imputation Considering Nonnegativity and Road Capacity

(2014) Huachun Tan et al.   MATHEMATICAL PROBLEMS IN ENGINEERING

Efficient missing data imputing for traffic flow by considering temporal and spatial dependence

(2013) Li Li et al.   TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES

A tensor-based method for missing traffic data completion

(2013) Huachun Tan et al.   TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES

Imputing Erroneous Data of Single-Station Loop Detectors for Nonincident Conditions: Comparison Between Temporal and Spatial Methods

(2012) Weihao Yin et al.   Journal of Intelligent Transportation Systems

Arterial travel time forecast with streaming data: A hybrid approach of flow modeling and machine learning

(2012) Aude Hofleitner et al.   TRANSPORTATION RESEARCH PART B-METHODOLOGICAL

Short-term traffic speed forecasting hybrid model based on Chaos–Wavelet Analysis-Support Vector Machine theory

(2012) Jin Wang et al.   TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES

Data Fusion Based Hybrid Approach for the Estimation of Urban Arterial Travel Time

(2012) S. P. Anusha et al.   Journal of Applied Mathematics

Statistical methods versus neural networks in transportation research: Differences, similarities and some insights

(2010) M.G. Karlaftis et al.   TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES

Traffic forecasting using least squares support vector machines

(2009) Yang Zhang et al.   Transportmetrica