ETS-Data
ETS-Data is jointly established by Tsinghua University Press and School of Vehicle and Mobility, Tsinghua University, China and is a publicly accessible database, providing indispensable materials for result replications (data, codes, scripts, simulations, experimental designs, etc.). ETS-Data has been indexed by DCI (Data Citation Index) and Google Dataset Search.
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Relation-Augmented Diffusion Graph Convolution Network (RADGCN)
This repository implements RADGCN, a relation-aware graph neural network for network-wide traffic state estimation (traffic kriging) when only a subset of road sensors is observed. It accompanies the manuscript Relation-diffusion augmented network-wide traffic state estimation.
Rather than learning adaptive relations from zero-filled features at unobserved nodes, RADGCN first learns functional relations among nodes with reliable observations. It then diffuses these relations across the directed road graph and uses the resulting relation kernel alongside forward and backward physical diffusion to reconstruct traffic speeds at unobserved nodes.
North America, United States Distilling Vision-Language Models for Explainable Vehicle Collision Prediction
This repository provides the code and supporting files for reproducing the experiments reported in “Distilling Vision-Language Models for Explainable Vehicle Collision Prediction.” It includes data-processing scripts for generating collision-prone and collision-free video clips from the MM-AU and CCD datasets, dataset manifests and annotations, VLM fine-tuning and knowledge-distillation scripts, and evaluation tools. The code was developed in a Linux environment using Python 3.10 and CUDA 11.8 or later. The required dependencies are listed in `requirements.txt`.
North America, United States Asia, China GSPINN: A Graph Sequential Physics-Informed Surrogate for Trip Travel Time Prediction
Europe, Germany, Cologne Europe, Monaco, Monaco Europe, Germany, Ingolstad Europe, Luxembourg, Luxemborg Multi-V2X (Replication Package for: Which2comm: An Efficient Collaborative Perception Framework with Connected and Automated Vehicles）
Multi-V2X is a large-scale, multi-modal, multi-penetration-rate dataset for cooperative perception under vehicle-to-everything (V2X) environment. Multi-V2X is gathered by SUMO-CARLA co-simulation and supports tasks including 3D object detection and tracking. Multi-V2X provides RGB images, point cloud from CAV and RSU with various CAV penetration rates (up to 86%). This package also includes the source code for the Which2comm framework proposed in the associated paper.
Asia, China, Beijing TWM: Multimodal data generation engine via Traffic World Model
The code is structured into two subdirectories, image_code and video_code, which respectively hold the full implementation of data generation, preprocessing, model training, inference and evaluation for the image and video modalities, with each subdirectory equipped with a separate README file that provides detailed environment setup and usage instructions. The runtime environment for this code is configured with Python 3.10, torch==2.6.0+cu126, and torchvision==0.21.0+cu126
Asia, China, Hangzhou 
Communications in Transportation Research was launched in 2021, with academic support provided by Tsinghua University and China Intelligent Transportation Systems Association. The Editors-in-Chief are Professor Xiaobo Qu, a member of the Academia Europaea from Tsinghua University and Professor Shuai’an Wang from Hong Kong Polytechnic University. The journal mainly publishes high-quality, original research and review articles that are of significant importance to emerging transportation systems, aiming to become an international platform and window for showcasing and exchanging innovative achievements in transportation and related fields, to promote the exchange and development of transportation research between China and the international academic community. It has been indexed in SCIE, SSCI, ESCI, Ei Compendex, Scopus, DOAJ, TRID and other databases. On June 20, 2024, Communications in Transportation Research achieved its first Impact Factor of 12.5, ranking it top in the "TRANSPORTATION" category (1/58, Q1), and its 2023 CiteScore of 15.2 places it in the top 5% of journals in the Scopus database.
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