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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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 Machine learning-based real-time crash risk forecasting for
pedestrians
This package contains R codes and python code as well as model input data to conduct replication. An explanatory file has also been provided.
Oceania, Australia, brisbane Replication Package for “What patterns contribute to autonomous vehicle crashes?” (L2 & L4, 2014–2024)
This replication package accompanies the manuscript “What patterns contribute to autonomous vehicle crashes? A study of Levels 2 and 4 automation using association rule analysis.”
It provides: (i) code to harmonize California DMV AV crash reports and NHTSA SGO crash reports into a unified, analysis-ready table; and (ii) scripts to run Apriori association rule mining with support/confidence/lift threshold
North America, United States, California (statewide) North America, United States, United States (nationwide, NHTSA SGO) Scalable and Reliable Multi-agent Reinforcement Learning for Traffic Assignment
This is the code and datasets for the potential publication "Scalable and Reliable Multi-agent Reinforcement Learning for Traffic Assignment".
Global 
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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