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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Replication package for Urba n rail transit resilience under different operation schemes: A percolation -based approach
The data are stored in Excel files, all in tabular format as structured data. Specifically: timetable data (train diagram data), routing data (train service plan data), running time (train travel duration), distance (inter-station distances), line net (network connectivity between lines), and position (actual geographical positions of stations). All aforementioned datasets are sourced from publicly available data provided by Beijing Subway or Baidu Map.
Asia, China, Beijing The dataset on the following behaviors of drivers on highways.
The highD dataset is a new dataset of naturalistic vehicle trajectories recorded on German highways. Using a drone, typical limitations of established traffic data collection methods such as occlusions are overcome by the aerial perspective. Traffic was recorded at six different locations and includes more than 110 500 vehicles. Each vehicle’s trajectory, including vehicle type, size and manoeuvres, is automatically extracted. Using state-of-the-art computer vision algorithms, the positioning error is typically less than ten centimeters. Although the dataset was created for the safety validation of highly automated vehicles, it is also suitable for many other tasks such as the analysis of traffic patterns or the parameterization of driver models.The dataset is extracted from the HighD dataset and pertains to the car type data, presented in the form of a tabular CSV file, which includes parameters such as the speed, acceleration, and longitudinal position of the ego vehicle, as well as the speed, acceleration, and longitudinal position of the preceding vehicle, among others. It can be utilized for studying driver following behavior.
Europe, Germany Project: A Risk-based Unmanned Aerial Vehicle Path Planning Scheme for Complex Air-Ground Environments
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].
Asia, China, Beijing Asia, China, Shanghai Asia, China, Chongqing Asia, China, Guangzhou Asia, China, Shenzhen Replication for "Should Autonomous Vehicles Be Subsidized to Reduce Parking Fees? A Productivity Perspective"
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.
Asia, China request data
Replication Package for "Compensation scheme and split delivery in a collaborative passenger-parcel transportation system"
Asia, China 
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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