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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Manuscript: A Cross-Temporal Framework for Assessing Driving Behavior's Impact on Electric Vehicle Battery Health.
This is the code and datasets for the potential publication "A Cross-Temporal Framework for Assessing Driving Behavior's Impact on Electric Vehicle Battery Health.".
Africa, China, Guangzhou North America, United States, Los Angeles North America, United States, New England FollowGen: A scaled noise conditional diffusion model for car-following trajectory prediction
This article presents FollowGen, a conditional diffusion model for vehicle trajectory prediction in car-following scenarios. Unlike existing diffusion-based approaches that introduce conditions only during the denoising stage, FollowGen incorporates a scaled noise conditioning mechanism in the forward process to embed historical motion features, and employs a cross-attention transformer in the reverse process to explicitly model interactions between leading and following vehicles. Experiments demonstrate that FollowGen consistently outperforms state-of-the-art baselines, achieving higher accuracy and robustness in diverse car-following environments.
Global Scalable and Interoperable C-V2X Framework for Real-time Intelligent Decision Support in Autonomous Mobility
To address the limited extensibility of standardized message format this study proposes a modular, edge-intelligent framework — The mobility Operating System (mOS) — integrated with a mixed-reality testbed for realistic validation of infrastructure-guided autonomous vehicle coordination. We analyzed to verify the feasibility of the C-V2X Framework for autonomous vehicle guidance in real-time at the physical testbed. The dataset was collected from the testbed experiment to analyze framework performance (speed profiles, post-encroachment time, and numerical error) and service performance (latency, jitter, and packet loss).
Asia, Korea, Republic Of, Daejeon Multimodal Network Data for Privacy-Preserving Traffic Assignment
This repository contains all necessary assets to replicate the study "Multimodal traffic assignment from privacy-protected OD data."
Location: /data
The dataset includes the following components:
Network Link Data: Parameters (cost, capacity, travel time) for each network link.
Route Sets: Pre-defined sequences of links (routes) analyzed in the case studies.
Implementation: The full PPTA model, privacy mechanism, and evaluation framework.
Language: Python 3.8+
Dependencies: Install required libraries:
pip install numpy pandas matplotlib scipy cvxpy
Note: The Mosek solver requires a separate license and installation.
Execution: Navigate to the PrivacyPreservingTrafficAssignment directory and run:
python main_ppta_new.py
This will execute the main script and reproduce the key results from the paper.
For a detailed explanation of the methodology, file descriptions, and step-by-step instructions, refer to:replication-explanatory-file-commtr-PPTA.docx
North America, United States Traffic congestion data in Alameda County in the San Francisco Bay Area, California
‘final_df.csv’ includes travel time index (TTI) and all potential influential factors for pre-lockdown, lockdown, and post-lockdown periods.
North America, United States 
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
