1710293276's Stars
Chengyuan-Zhang/IDM_Bayesian_Calibration
bilzinet/Traffic-state-reconstruction-using-Deep-CNN
We propose a statistical learning-based traffic speed estimation method that uses sparse vehicle trajectory information. Using a convolutional encoder-decoder based architecture, we show that a well trained neural network can learn spatio-temporal traffic speed dynamics from timespace diagrams. We demonstrate this for a homogeneous road section using simulated vehicle trajectories and then validate it using real-world data from NGSIM. Our results show that with probe vehicle penetration levels as low as 5%, the proposed estimation method can provide a sound reconstruction of macroscopic traffic speeds and reproduce realistic shockwave patterns, implying applicability in a variety of traffic conditions. We further discuss the model’s reconstruction mechanisms and confirm its ability to differentiate various traffic behaviors such as congested and free-flow traffic states, transition dynamics, and shockwave propagation.
cosbidev/PyTrack
a Map-Matching-based Python Toolbox for Vehicle Trajectory Reconstruction
rpy2/rpy2-docker
Running rpy2 in Docker containers
GPflow/GPflow
Gaussian processes in TensorFlow
cagrell/gp_constr
Python model for constrained GP
gpstuff-dev/gpstuff
GPstuff - Gaussian process models for Bayesian analysis
esiivola/gpstuff_monotonic
repository for developing monotonicity features to gpstuff without them being public
licit-lab/Open-SymuVia
A microscopic traffic simulator
pipposheng/CarFollowingModel
Simulation of Car-Following Model using GM First Car-Following Model
thoenselaar/car-following-model
Car following model created in Matlab/Simulink using the Intelligent Driver Model (IDM)