Project - Traffic forecasting by spatio-temporal graph model

Final Degree Project

ST-MAN: Spatio-Temporal Multimodal Attention Network for Traffic Prediction (KSEM 2023)

This repository contains code and resources for a project focused on predicting traffic volume using Temporal Convolutional Networks (TCNs). Leveraging the Metro Interstate Traffic Volume dataset from 2012-2018, the project aims to develop an accurate model for short- to medium-term traffic volume forecasting in Minneapolis-St Paul, MN.

BIND: Binding Intertemporal Nodes for Multivariate Timeseries Forecasting (Ongoing)

An eXplainable AI system to elucidate short-term speed forecasts in traffic networks obtained by Spatio-Temporal Graph Neural Networks.

[CIKM 2023] This is the official source code of "TrendGCN: Enhancing the Robustness via Adversarial Learning and Joint Spatial-Temporal Embeddings in Traffic Forecasting" based on Pytorch.

Computation of dynamic selfish flows using predicted travel times

LogiTraffic is an IoT based Deep Learning Powered Traffic Management and Theft Detection Solution. It’s an online website platform using which user can keep check on certain real-time parameters associated with the vehicle which includes fuel-level, GPS location, Brake System Temperature, Speed, Traffic Forecasting using Vehicle Detection and ob…

Repository for the paper "Graph Convolutional Networks for Traffic Forecasting with Missing Values" in DMKD'22

A PyTorch implementation of the Attention Diffusion Network from "Structured Time Series Prediction without Structural Prior"

Model-based AI approach for network and service coordination leveraging uncertain traffic forecasts

Graph Neural Networks utilization for Spatiotemporal graphs. These methods will be applied into the problem of forecasting traffic flow on PEMS-Bay, METR-LA and Seattle Loop Datasets

Official repo for the following paper: Traffic Forecasting on New Roads Unseen in the Training Data Using Spatial Contrastive Pre-Training (SCPT) (ECML PKDD DAMI '23)

[IJCAI'2022] FOGS: First-Order Gradient Supervision with Learning-based Graph for Traffic Flow Forecasting

[ICDE'2023] When Spatio-Temporal Meet Wavelets: Disentangled Traffic Forecasting via Efficient Spectral Graph Attention Networks

Source codes of CIKM2022 Full Paper "Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Cities"

[IJCNN 2021] Unified Spatio-Temporal modeling for traffic forecasting using Graph Convolutional Network

[PAKDD 2021] SST-GNN: Simplified Spatio-temporal Traffic forecasting model using Graph Neural Network

STGM: Spatio-Temporal Graph Mixformer for Traffic Forecasting