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Simple and effective tools for the analysis of movement data
Different implementations of Bayesian neural networks for uncertainty estimation. The uncertainty estimation is utilized for efficient exploration in reinforcement learning.
Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.
Solutions of Reinforcement Learning, An Introduction
A PyTorch implementation of the Light Temporal Attention Encoder (L-TAE) for satellite image time series. classification
Code for a car-following model that predicts the acceleration of a car for the next time step based on headway and velocity data.
Code for an optimal velocity model (OVM) and a multiple car following (MCF) model
An eXplainable AI system to elucidate short-term speed forecasts in traffic networks obtained by Spatio-Temporal Graph Neural Networks.
Driving models: IDM, OVM, IFVDM, Newells, Gipps
Vehicle Trajectory Prediction with Deep Learning Models
Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting, AAAI 2019, pytorch version
tsl: a PyTorch library for processing spatiotemporal data.
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting, which is accepted at ICML2022.
Python implementation of Gipps' Car-Following model.
Code for our VLDB'22 paper Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting.
Source code for paper "FollowNet: A Comprehensive Benchmark for Car-Following Behavior Modeling"
Python traffic simulation libarary based on Martin Treiber's Java applet from: https://www.traffic-simulation.de/. Implement IDM follower model and Lanechange model.
Temporal Graph Convolutional Network for Urban Traffic Flow Prediction Method
A toolkit for developing and comparing reinforcement learning algorithms.