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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.

Latest Update

List

  • Published on: 2024-11-14Updated on: 2025-03-27 Associated article: https://doi.org/10.1111/risa.17685

    Project: A Risk-based Unmanned Aerial Vehicle Path Planning Scheme for Complex Air-Ground Environments

    Kai Zhou, Kai Wang, Yuhao Wang, Xiaobo Qu

    Overview
    It is a Python-based application aimed at designing and optimizing air corridors for efficient air traffic management. This project leverages various third-party risks to create safe and efficient air routes.

    Features
    - Route Optimization
    - Safety Analysis
    - Data Visualization

    Installation
    To get started with the Air Corridor Design project, follow these steps:

    1. Download the data set.
    2. Navigate to the project directory:
        cd ./
    3. Install the required dependencies:
        pip install -r requirements.txt

    Usage
    To run the application, use the following command:
    python ./Air_Corridor_Design/main.py

    Project Structure
    - README.md: This file.
    - requirements.txt: List of dependencies required for the project.
    - setup.py: Script for completing setup.
    - Air_Corridor_Design/: Directory containing the source code.
    - Data/: Directory containing data files used by the application.
    - Experiments/: Directory containing experimental results.
    - Scripts/: Directory containing some scripts for testing.

    Contact
    For any questions or suggestions, please contact [zhouk23@mails.tsinghua.edu.cn].

    UamPath planning
    DOI: 10.26599/ETSD.2024.9190032
    CSTR: 32009.11.ETSD.2024.9190032.V4
    Asia, China, Beijing Asia, China, Shanghai Asia, China, Chongqing Asia, China, Guangzhou Asia, China, Shenzhen
  • Published on: 2025-03-25

    Replication for "Should Autonomous Vehicles Be Subsidized to Reduce Parking Fees? A Productivity Perspective"

    Yao Li, Ziyue Yang, Shuxian Xu, Tao Wang, Jiancheng Long

    The files is the replication for study of "Should Autonomous Vehicles Be Subsidized to Reduce Parking Fees? A Productivity Perspective". The readers can replicate the study using GAMS to solve the non-linear program.

    Autonomous vehiclesCity
    DOI: 10.26599/ETSD.2025.9190011
    CSTR: 32009.11.ETSD.2025.9190011
    Asia, China
  • Published on: 2025-03-25

    request data

    Yitong Yu

    Replication Package for "Compensation scheme and split delivery in a collaborative passenger-parcel transportation system"

    Collaborative passenger-parcel transportVehicle routing
    DOI: 10.26599/ETSD.2025.9190010
    CSTR: 32009.11.ETSD.2025.9190010
    Asia, China
  • Published on: 2025-03-20

    MetaSSC: Enhancing 3D Semantic Scene Completion for Autonomous Driving through Meta-Learning and Long-sequence Modeling 

    Yansong Qu, Zixuan Xu, Zilin Huang, Zihao Sheng, Sikai Chen, Tiantian Chen

    Our approach begins with a voxel-based semantic segmentation (SS) pretraining task, designed to explore the semantics and geometry of incomplete regions while acquiring transferable meta-knowledge. Using simulated cooperative perception datasets, we supervise the training of a single vehicle’s perception using the aggregated sensor data from multiple nearby connected autonomous vehicles (CAVs), generating richer and more comprehensive labels. This meta-knowledge is then adapted to the target domain through a dual-phase training strategy—without adding extra model parameters—ensuring efficient deployment. To further enhance the model’s ability to capture long-sequence relationships in 3D voxel grids, we integrate Mamba blocks with deformable convolution and large-kernel attention into the backbone network. Extensive experiments show that MetaSSC achieves state-of-the-art performance, surpassing competing models by a significant margin, while also reducing deployment costs. 

    Connected and automated vehiclesAutonomous vehicles
    DOI: 10.26599/ETSD.2025.9190009
    CSTR: 32009.11.ETSD.2025.9190009
    North America, United States
  • Published on: 2025-03-18

    Road Network Data in Wangjing District, Beijing, China

    Xiangdong Chen, Shen Li, Meng Li

    NodeSet_2D.mat’ and LinkSet_2D.mat’ describe the node set and link set in Wangjing road network, and ‘ODset.mat’ describe the O-D pairs of demand for UAVs.

    Road transportOd
    DOI: 10.26599/ETSD.2025.9190008
    CSTR: 32009.11.ETSD.2025.9190008
    Asia, China, Beijing
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Journal
Overview

Communications in Transportation Research

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.

Indexed by international databases