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Physics Informed Deep Generative Model for Vehicle Trajectory Reconstruction at Arterial Intersections in Connected Vehicle Environment

https://doi.org/10.2139/ssrn.4760708
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inferredcites →https://doi.org/10.1002/atr.1260openalex-extracted
Vehicle trajectory reconstruction using automatic vehicle identification and traffic count data
inferredcites →https://doi.org/10.1007/jhep10(2022)001openalex-extracted
Non-parametric data-driven background modelling using conditional probabilities
inferredcites →https://doi.org/10.1007/s10596-020-09978-xopenalex-extracted
Generative adversarial network as a stochastic subsurface model reconstruction
inferredcites →https://doi.org/10.1016/j.eswa.2023.120393openalex-extracted
Signal-control refined dynamic traffic graph model for movement-based arterial network traffic volume prediction
inferredcites →https://doi.org/10.1016/j.eswa.2023.120535openalex-extracted
Multi-Objective Traffic Signal Control Using Network-Wide Agent Coordinated Reinforcement Learning
inferredcites →https://doi.org/10.1016/j.eswa.2024.124298openalex-extracted
A time-varying shockwave speed model for reconstructing trajectories on freeways using Lagrangian and Eulerian observations
inferredcites →https://doi.org/10.1016/j.jcp.2018.10.045openalex-extracted
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
inferredcites →https://doi.org/10.1016/j.physa.2021.126788openalex-extracted
TSR-GAN: Generative Adversarial Networks for Traffic State Reconstruction with Time Space Diagrams
inferredcites →https://doi.org/10.1016/j.physa.2022.127610openalex-extracted
Trajectory research of Cellular Automaton Model based on real driving behaviour
inferredcites →https://doi.org/10.1016/j.physa.2022.128243openalex-extracted
A general dynamic sequential learning framework for vehicle trajectory reconstruction using automatic vehicle location or identification data
inferredcites →https://doi.org/10.1016/j.trc.2011.05.006openalex-extracted
Implementing kinematic wave theory to reconstruct vehicle trajectories from fixed and probe sensor data
inferredcites →https://doi.org/10.1016/j.trc.2013.09.002openalex-extracted
Vehicle trajectory reconstruction for signalized intersections using mobile traffic sensors
inferredcites →https://doi.org/10.1016/j.trc.2016.01.010openalex-extracted
Reconstructing maximum likelihood trajectory of probe vehicles between sparse updates
inferredcites →https://doi.org/10.1016/j.trc.2018.05.009openalex-extracted
A generic data assimilation framework for vehicle trajectory reconstruction on signalized urban arterials using particle filters
inferredcites →https://doi.org/10.1016/j.trc.2019.01.026openalex-extracted
Urban traffic signal control with connected and automated vehicles: A survey
inferredcites →https://doi.org/10.1016/j.trc.2019.12.002openalex-extracted
Trajectory reconstruction for freeway traffic mixed with human-driven vehicles and connected and automated vehicles
inferredcites →https://doi.org/10.1016/j.trc.2020.02.016openalex-extracted
Trajectory data-based traffic flow studies: A revisit
inferredcites →https://doi.org/10.1016/j.trc.2022.103929openalex-extracted
Integrated macro-micro modelling for individual vehicle trajectory reconstruction using fixed and mobile sensor data
inferredcites →https://doi.org/10.1016/s1369-8478(00)00005-xopenalex-extracted
Car-following: a historical review
inferredcites →https://doi.org/10.1049/itr2.12294openalex-extracted
Trajectory reconstruction for mixed traffic flow with regular, connected, and connected automated vehicles on freeway
inferredcites →https://doi.org/10.1080/17499518.2023.2222383openalex-extracted
Non-parametric simulation of random field samples from incomplete measurements using generative adversarial networks
inferredcites →https://doi.org/10.1080/21680566.2020.1781707openalex-extracted
Vehicle trajectory reconstruction for signalized intersections: A hybrid approach integrating Kalman Filtering and variational theory
inferredcites →https://doi.org/10.1080/21680566.2024.2315509openalex-extracted
Second-based queue length estimation with fusing MMW and low penetration rate CAV trajectory data
inferredcites →https://doi.org/10.1103/physreve.62.1805openalex-extracted
Congested traffic states in empirical observations and microscopic simulations
inferredcites →https://doi.org/10.1109/cvpr.2018.00781openalex-extracted
Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
inferredcites →https://doi.org/10.1109/iccv48922.2021.01291openalex-extracted
MG-GAN: A Multi-Generator Model Preventing Out-of-Distribution Samples in Pedestrian Trajectory Prediction
inferredcites →https://doi.org/10.1109/itsc.2014.6957877openalex-extracted
Probabilistic model for estimating vehicle trajectories using sparse mobile sensor data
inferredcites →https://doi.org/10.1109/mvt.2015.2508322openalex-extracted
Connected-Vehicles Applications Are Emerging [Connected Vehicles]
inferredcites →https://doi.org/10.1109/tits.2019.2955794openalex-extracted
TrafficGAN: Network-Scale Deep Traffic Prediction With Generative Adversarial Nets
inferredcites →https://doi.org/10.1109/tits.2020.2976671openalex-extracted
A Particle Filter-Based Approach for Vehicle Trajectory Reconstruction Using Sparse Probe Data
inferredcites →https://doi.org/10.1109/tits.2021.3126621openalex-extracted
Cooperative Perception for Estimating and Predicting Microscopic Traffic States to Manage Connected and Automated Traffic
inferredcites →https://doi.org/10.1109/tits.2022.3150577openalex-extracted
Vehicle Trajectory Reconstruction at Signalized Intersections Under Connected and Automated Vehicle Environment
inferredcites →https://doi.org/10.1111/j.1467-8667.2009.00617.xopenalex-extracted
A Robust and Efficient Method for Fusing Heterogeneous Data from Traffic Sensors on Freeways
inferredcites →https://doi.org/10.1137/21m1457448openalex-extracted
Generative Adversarial Network for Probabilistic Forecast of Random Dynamical Systems
inferredcites →https://doi.org/10.1155/2021/6181242openalex-extracted
An Unscented Kalman Filter-Based Method for Reconstructing Vehicle Trajectories at Signalized Intersections
inferredcites →https://doi.org/10.2139/ssrn.3994303openalex-extracted
A General Dynamic Sequential Learning Framework to Vehicle Trajectory Reconstruction Using Automatic Vehicle Location or Identification Data
inferredcites →https://doi.org/10.3141/2260-17openalex-extracted
Wiedemann Revisited
inferredcites →https://doi.org/10.3141/2390-11openalex-extracted
Making NGSIM Data Usable for Studies on Traffic Flow Theory
inferredcites →https://doi.org/10.3390/rs14092033openalex-extracted
WHUVID: A Large-Scale Stereo-IMU Dataset for Visual-Inertial Odometry and Autonomous Driving in Chinese Urban Scenarios
inferredcites →https://doi.org/10.48550/arxiv.1511.01844openalex-extracted
A note on the evaluation of generative models
inferredcites →https://doi.org/10.48550/arxiv.1810.04650openalex-extracted
Multi-Task Learning as Multi-Objective Optimization
inferredcites →https://doi.org/10.6339/21-jds1004openalex-extracted
Large Scale GPS Trajectory Generation Using Map Based on Two Stage GAN
inferredcites →https://doi.org/10.13140/rg.2.2.24614.24643openalex-snapshot
Cooperative Driving in Mixed-flow Traffic of Connected Vehicles and Human-driven Vehicles: A State Estimation Approach
inferredcites →https://doi.org/10.48550/arxiv.2105.13010openalex-snapshot
An error analysis of generative adversarial networks for learning distributions
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