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
Replication package of the Paper titled a trajectory planning and tracking method based on deep hierarchical reinforcement learning
All the materials source coding and results coding of the Paper titled a trajectory planning and tracking method based on deep hierarchical reinforcement learning
Global Replication package of Enhancing Driver Emotion Recognition Through Deep Ensemble Classification.
All the materials source coding and results coding of Enhancing Driver Emotion Recognition Through Deep Ensemble Classification.
Asia, China Extracting networkwide road segment location, direction, and turning movement rules from global positioning system vehicle trajectory data for macrosimulation
The replication package dataset (OpenstreetMap study network, GPS raw data, and intersection locations) used for the study.
Global 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 Traffic oscillations mitigation with physics-enhanced residual learning (PERL)-based predictive control
This repository supports the research on mitigating traffic oscillations using a Physics-Enhanced Residual Learning (PERL)-based predictive control approach. It contains all necessary components related to both the prediction and control aspects of the study. The prediction module includes the pre-processed NGSIM dataset, prediction models, and the resulting predictions, which focus on forecasting the behavior of preceding vehicles, including speed fluctuations, to allow timely responses. The control module implements a Model Predictive Control (MPC) approach that uses the prediction results to control connected and automated vehicles (CAVs), enhancing safety and comfort in mixed traffic environments. All code, data, and results are included to ensure that users can replicate the experiments and validate the findings effectively.
North America, United States 
Communications in Transportation Research publishes peer-reviewed high-quality research representing important advances of significance to emerging transport systems. The mission is to provide fair, fast, and expert peer review to authors and insightful theories, impactful advances, and interesting discoveries to readers. We welcome submissions of significant and general topics, of inter-disciplinary nature (transport, civil, control, artificial intelligence, social science, psychological science, medical services, etc.), of complex and inter-related system of systems, of strong evidence of data strength, of visionary analysis and forecasts towards the way forward, and of potentially implementable and utilizable policies/practices. It is indexed in Scopus and DOAJ.
Indexed by international databases
