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Guide for authors

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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  • Published on: 2026-10-27 Associated article: https://doi.org/10.26599/JICV.2026.9210102

    AdaFusionNet: Adaptive Trajectory Prediction of Group Two-wheelers in Interactive Conflict Scenarios for Autonomous Vehicles

    yuanhao liu

    AdaFusionNet replication package for the article (DOI: 10.26599/JICV.2026.9210102). The package contains STTransformer, RSFM, and GRU fusion code, together with utilities for interaction-scene extraction, recording-level data preparation, sample-window construction, auditing, and prediction alignment. The data directory contains 110 cleaned car and two-wheeler trajectory CSVs. The study uses 45 recording pairs from Tianjin, Changchun, and Xi’an. The additional ChongqingNR files are retained in the source collection but are not used in the study. Install requirements.txt and run ./scripts/run_pipeline.ps1 from the package root. See README.md and REPLICATION_EXPLANATORY_FILE.md for data formats, processing steps, and outputs. Software and documentation are licensed under MIT; trajectory-data rights remain with the data provider.

    Automated vehicleTrajectory prediction
    DOI: 10.26599/ETSD.2026.9190113
    CSTR: 32009.11.ETSD.2026.9190113
    Asia, China
  • Published on: 2026-10-27

    Integrated vehicle and bidirectional charging scheduling for V2G-enabled electric bus system

    Yiming Bie, Yuman Xu, Ao Xue, Jinhua Ji, Menglin Yang, Jue Zhou

    The replication package associated with this study is publicly available at ETS-Data. The package contains the input data, source codes, parameter files, and computational results. The data used in this study describe the daily operation of a real-world multi-route electric bus system. The data were provided in JSON format in the replication package and named as dataConfig. 

    Electric busV2x
    DOI: 10.26599/ETSD.2026.9190111
    CSTR: 32009.11.ETSD.2026.9190111
    Asia, China, Changchun
  • Published on: 2026-10-27

    Capacity Drop as a Phase Transition: An Analytical Model via Stochastic Disturbance Propagation

    Xiaohui Zhang

    The replication package of this paper

    Traffic flowCar-following interaction
    DOI: 10.26599/ETSD.2026.9190112
    CSTR: 32009.11.ETSD.2026.9190112
    Global
  • Published on: 2026-10-27

    Replication Package for "CTM-augmented Proximal Policy Optimization for Joint Lane-Changing and Speed Advisory Control at Urban Lane-Drop Bottlenecks under Heterogeneous Traffic"

    Xiaoge Cao, Mengchi Cai, Yitang Wang, Ye He, Gang Li, Guang Chen, Xiangdong Chen, Meng Li, Bolin Gao

    This replication package includes SUMO microscopic simulation source codes, network and route configuration files, the MPC baseline controller, the online CTM calibration module, and PPO training scripts. It also contains full experimental results covering 10 random seeds, 3 flow conditions, 2 traffic scenarios and 5 algorithms to back up the results in this manuscript. The attached Replication Explanatory File provides step-by-step guidance to reproduce Tables 4–5 and Figures 3–8.

    Connected and automated vehiclesDeep reinforcement learningModel predictive control
    DOI: 10.26599/ETSD.2026.9190111
    CSTR: 32009.11.ETSD.2026.9190111
    Asia, China, Beijing
  • Published on: 2026-10-27

    FGA-BEVFusion: Frequency-Guided Attention for LiDAR–Camera BEV Fusion in 3D Object Detection

    Zechang Ye

    This replication package indudesthe complete code used in ourstudy titled “FGA-BEVFusion: Frequency-Guided Attention for LiDAR–Camera BEV Fusion in 3D Object Detection”. The supplied materials are of great significance to researchers and practitioners who aim to reproduce our experiments and verify the corresponding research results.

    Autonomous vehiclesAutonomous driving
    DOI: 10.26599/ETSD.2026.9190110
    CSTR: 32009.11.ETSD.2026.9190110
    North America, United States, Boston
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Journal
Overview

Communications in Transportation Research

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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