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: 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 Beyond Conventional Vision: RGB-Event Fusion for Robust Object Detection in Dynamic Traffic Scenarios
The code is used in the paper:"Beyond Conventional Vision: RGB-Event Fusion for Robust Object Detection in Dynamic Traffic Scenarios"
Global Can combined virtual-real testing speed up autonomous vehicle testing? Findings from AEB field experiments
This replication package includes the complete code and dataset used in our study titled “Can Combined Virtual-Real Testing Speed Up Autonomous Vehicle Testing? Findings from AEB Field Experiments." The materials provided are essential for researchers and practitioners interested in replicating our experiments and validating the findings.
Asia, China The dataset of the paper "MoTIF: An end-to-end Multimodal Road Traffic Scene Understanding Foundation Model"
该数据集包括 2023 年 10 月城市十字路口四个方向的监控视频。 这些视频的分辨率为 3840×2160，帧速率为每秒 30 帧 （fps），场景理解标注涵盖交通拥堵程度、行人和车辆数量、行为意图等正常场景。
Asia, China, 天津 
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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