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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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 Leveraging distributed acoustic sensing for large-scale expressway traffic state perception
This replication package comprises two complementary components: (1) a MATLAB‑based DAS simulation framework that generates synthetic acoustic signals and spatiotemporal traffic maps from microscopic vehicle trajectories, and (2) a Python implementation of physics‑informed neural networks (PINNs) for traffic state restoration, covering 16 model variants with different architectures (ResUNet and FNO) and physical constraints (LWR, LWR+FD, and ARZ). The package includes simulation input files (trajectory data and SUMO road network), a tutorial video for the simulation workflow, pre‑trained weights for two‑channel inputs, and sample training data. The full dataset and additional three‑channel model weights are available via a separate download link.
Asia, China, Hefei Evolving Cooperative Controllers for CAVs at Unsignalized Intersections via the LLM-as-Designer Paradigm
A project for a study on LLM-designed cooperative control strategies for connected and automated vehicles at unsignalized intersections. It includes the Python source code, SUMO simulation files, processed scenario data, experiment logs, and documentation needed to reproduce and inspect the main computational experiments.
North America, United States Ego vehicle trajectory prediction based on driver-vehicle coupling characteristics in diverse urban scenarios
This software package supports the reproduction of key numerical results reported in the study, including scripts that use driver-vehicle coupling characteristics as input to predict the ego-vehicle’s trajectory in diverse urban scenarios, as well as script for evaluating the performance of the predicted trajectories.
The package includes processed data & normalization parameters, ego vehicle trajectory prediction deep learning training models (DriVETP, DriVETP-Ablation1, DriVETP-Ablation2, AM-LSTM, Transformer), visulaization results from multi-scenario testing, performance metrics (RMSE, ADE, FDE) radar plots, and model generalization validation.
Test scenarios cover typical urban driving conditions: left lane changing, right lane changing, roundabout navigation, left turning, right turning, and straight driving. Detailed instructions for reproducing the main results are provided in the README file.
Asia, China, Changchun Towards the Intelligence Evaluation of High-level Autonomous Vehicles: A Subjective-Objective Mapping Method via LLM
This repository provides a lightweight reproduction of an autonomous-driving intelligence evaluation workflow. It evaluates vehicle interactions from five dimensions, combines ten normalized objective metrics with LLM-based subjective scores, and trains an attention-based neural network to approximate the final intelligence score.
The repository includes 705 indexed Waymo interaction samples and a lightweight Waymo/trajdata cache. It can therefore reproduce the main workflow without downloading the complete Waymo or InterHub dataset.
Africa, China North America, United States 
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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