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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Vehicle Platoon Trajectory Prediction Under Traffic Oscillation: A Causal Physics-Informed Deep Learning Approach
In the data file, NGSIM refers to the NGSIM trajectory data mentioned in the paper.
In the data file, WHUT contains the TVD trajectory data.
All code is stored in the “code” folder.
The filenames in the “code” folder correspond to the comparison model code and my complete model code, respectively.
Code Execution Order:
1. First, set up a virtual environment according to the requirements in `requirements.txt`.
2. Use the code in the “Data Preprocessing” section to build a standard training dataset. Note that you must manually adjust the prediction step size in the dataset to match that of each code file to ensure consistency.
3. Place the constructed data in a location accessible to all scripts, then run each script individually. To facilitate execution by readers, each .py file includes the model architecture, training code, testing code, validation code, and code for visualizing and saving results.
Asia, China, WUHAN This dataset provides field-collected data from an electric vehicle (EV) platoon, specifically curated to analyze car-following behavior across different driving modes and control types.
By capturing data under both One-Pedal Driving (OPD) and Two-Pedal Driving (TPD)—and comparing Manual Driving against Adaptive Cruise Control (ACC)—the dataset offers a comprehensive look at how EV technology influences traffic flow.
Vehicle Composition: The data tracks a four-vehicle platoon consisting of one Internal Combustion Engine (ICE) lead vehicle followed by three EVs.
Data Resolution: All trajectories are recorded at a high-frequency 10 Hz sampling rate.
Sensor Integration: The dataset includes synchronized GPS trajectories for all four vehicles, ensuring precise spatial and temporal alignment.
North America, United States, Athens Can Large Language Models Capture Human Risk Preferences? A Cross-Cultural Study
Due to privacy constraints, we have provided anonymized samples with randomly shuffled data distributions to illustrate the data structure used in our experiments.
Asia, China Oceania, Australia Replication Package for COIN: Collaborative Interaction-Aware Multi-Agent Reinforcement Learning for Self-Driving Systems
This repository is the replication package for COIN: Collaborative Interaction-Aware Multi-Agent Reinforcement Learning for Self-Driving Systems. It provides the codebase, dependencies, and instructions needed to reproduce the training and evaluation results of COIN in multi-agent self-driving scenarios.
Global Examining driving behaviors and trust in an in-vehicle warning system under uncertainty: A roundabout study
ABSTRACT: Advanced driver assistance systems (ADASs) can greatly enhance road safety by providing real-time warnings to drivers in imminent crash situations. However, the provided warning time may deviate from its designed time. There is limited research on how warning uncertainties influence drivers’ behavior, safety performance, and trust. This study conducted a driving simulator study to examine how uncertainties in warnings impact driving behaviors and trust using a roundabout driving scenario. Two warning error distributions were constructed to represent low and high warning uncertainty levels. Thirty-six participants were recruited and randomly divided into two groups under the two uncertainty levels in a driving simulator experiment. The betweengroup analysis shows that the lower warning uncertainty level group results in higher trust and that trust increases (or decreases) over time under low (or high) uncertainty levels. The within-group analysis shows that higher warning errors downgrade drivers’ trust and safety performance when the errors are high.
North America, United States, West Lafayette 
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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