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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Efficient and Stable Ride-Pooling through a Multi-Level Coalition Formation Game
This replication package contains the source code, configuration files, input data, and result samples for the paper “Efficient and stable ride-pooling through a multi-level coalition formation game”. The package allows users to reproduce all experiments reported in the article. Instructions for environment setup and execution are provided in the included README and configuration files.
Asia, China, Ningxia Asia, China, Chengdu Asia, China, Haikou Manuscript: Quantitative assessment of mid-air collision probability in urban air mobility: A safety barrier-based framework for integrated operations
This is the code and datasets for the paper "Quantitative assessment of mid-air collision probability in urban air mobility: A safety barrier-based framework for integrated operations". This research proposes a method to systematically quantify the mid-air collision (MAC) risk for different operation types in urban air mobility (UAM).
Global Humanoid Cognition-Based Approach: Lane-Changing Decision Making and Dynamic Trajectory Planning for Autonomous Driving
This study proposes a lane-changing decision and trajectory planning algorithm for intelligent vehicles on highways that takes driver behavior into consideration. A co-simulation model based on Prescan and Simulink was built to validate the designed trajectory planning algorithm. The code and data related to the proposed algorithm and its verification are also provided.
Global Manuscript: Deep Reinforcement Learning-Based Eco-Driving: Bridging Short-Term Energy-Efficient and Long-Term Battery-Health-Aware Strategy in Dynamic Driving Cycles
This is the code and datasets for the potential publication "Deep Reinforcement Learning-Based Eco-Driving: Bridging Short-Term Energy-Efficient and Long-Term Battery-Health-Aware Strategy in Dynamic Driving Cycles".
Africa, China, Guangzhou North America, United States, Los Angeles North America, United States, New England Towards fair lights: A multi-agent masked deep reinforcement learning for efficient corridor-level traffic signal control
The is the code for paper "Towards fair lights: A multi-agent masked deep reinforcement learning for efficient corridor-level traffic signal control". Run train.py for deep reinforcement learning model training. Run test.py for testing.
Oceania, Australia, Melbourne 
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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