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Guide for authors

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: 2024-12-24 Associated article: https://doi.org/10.26599/JICV.2023.9210046

    Extracting networkwide road segment location, direction, and turning movement rules from global positioning system vehicle trajectory data for macrosimulation

    Adham Badran, Ahmed El-Geneidy, Luis Miranda-Moreno

    The replication package dataset (OpenstreetMap study network, GPS raw data, and intersection locations) used for the study. 

    Path planningGps
    DOI: 10.26599/ETSD.2024.9190033
    CSTR: 32009.11.ETSD.2024.9190033
    Global
  • Published on: 2024-11-14Updated on: 2024-12-23 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.V3
    Asia, China, Beijing Asia, China, Shanghai Asia, China, Chongqing Asia, China, Guangzhou Asia, China, Shenzhen
  • Published on: 2024-10-29

    Traffic oscillations mitigation with physics-enhanced residual learning (PERL)-based predictive control

    Keke Long, Zhaohui Liang, Haotian Shi, Lei Shi, Sikai Chen, Xiaopeng Li

    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.

     

    Automated vehicleConnected vehicle
    DOI: 10.26599/ETSD.2024.9190031
    CSTR: 32009.11.ETSD.2024.9190031
    North America, United States
  • Published on: 2024-09-09

    Bidirectional Q-Learning for recycling path planning of used appliances under strong and weak constraints

    Yang Qi, Jinxin Cao, Baijing Wu

    The Layered Bidirectional Q-Learning (LBQ) algorithm is designed for path planning, tackling the complexities inherent in multilayer path planning during the recycling process. This approach incorporates a bidirectional update mechanism that minimizes the unpredictability associated with initial exploration phases. Additionally, the algorithm employs a hierarchical reinforcement learning strategy, which breaks down intricate tasks into more manageable subtasks. Through the strategic design of reward functions that address various constraints, the LBQ algorithm successfully optimizes paths under multiple conditions.

    Path planningQ-learning
    DOI: 10.26599/ETSD.2024.9190030
    CSTR: 32009.11.ETSD.2024.9190030
    Asia, China
  • Published on: 2024-08-07

    Collaborative electric vehicle routing with meet points

    Jiaming Wu, Fangting Zhou, Ala Arvidsson, Balazs Kulcsar

    The replication file contains data used in the paper "Collaborative electric vehicle routing with meet points" published in COMMTR. In this paper, we use real-world locations of grocery stores in Gothenburg, Sweden. The original data is the real addresses. Additionally, we use some test location data to evaluate large-scale instance performance, with customer locations randomly generated within a 25 km × 25 km region. The x and y coordinates range from 0 to 25 and the unit is kilometer. In addition to customer locations, we have included meet point and depot locations in the file. The type of each location and its associated company are noted. This data has been prepared for replication purposes. 
    The code for the proposed algorithms is currently being used in another paper, which is under review. We will upload the code once the other paper is published. 

    Charging scheduleElectric vehicleFreight
    DOI: 10.26599/ETSD.2024.9190029
    CSTR: 32009.11.ETSD.2024.9190029
    Europe, Sweden, Gothenburg
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Journal
Overview

Communications in Transportation Research

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