All materials related to this study have been made available through ETS Data and an external public data repository for transparency and reproducibility purposes. The ETS Data replication package contains the main output results reported in the manuscript, the source code, and the corresponding computational environment configuration files. Due to its large size, the complete raw experimental dataset is hosted separately on Hugging Face and is publicly accessible at: https://huggingface.co/datasets/InterestingITS/smartphone_in_vehicle_multisensor

Bus stops vary widely in the conditions they provide to waiting passengers, yet ridership analysis rarely considers these differences alongside service performance. This study examines how stop-level amenities relate to bus ridership, conditional on service frequency and schedule deviation. 

This replication package accompanies the revised JICV manuscript, "Vehicle Platooning and Signal Coordination for Intelligent Connected Intersections Toward Autonomous Transportation Systems." It contains original and supplementary SUMO code, 1,000/760-m networks, processed DLR traffic-demand inputs, routes, signal plans, MA10/LSTM prediction assets, and scripts. It covers Fixed-time, Platoon-only, Adaptive-only, Joint, and bounded MaxPressure controllers at demand scales 1.0, 1.2, and 1.5; prediction sensitivity, LSTM substitution, shorter approaches, and multi-seed robustness. New evidence includes seven feedback-matched Signal-only runs, 30 controlled platoon-release trials, 92 archived completed-platoon events, and a 782-check design-speed audit. The README documents dependencies, setup, commands, and reproduction of delay, queues, throughput, clearance, safety metrics, tables, and figures. DLR-derived inputs retain CC BY-NC-SA 4.0; other files retain their respective licenses.

This replication package accompanies the manuscript "DS-MPC: An integrated decision-making and control framework for autonomous driving in urban environments", submitted to the Journal of Intelligent and Connected Vehicles (Manuscript ID: JICV-2026-0084.R1). It contains the DS-MPC source code, CARLA scenario initialization files, the GPR prediction model, experimental configurations, archived results, and a Replication Explanatory File. The materials cover four urban driving scenarios and support the evaluation of safety, task completion, computational performance, component ablations, and horizon sensitivity. The explanatory file describes the package structure, software requirements, execution procedures, result mappings, and known limitations.

Describe the public's psychological perceptions and willingness to pay for CV applications. For psychological perceptions toward CV applications, we measured participants' perceived usefulness, trust, attitudes, perceived risk, and behavioral intentions toward CV applications.

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.

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. 

The replication package of this paper

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.

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.

The file is code for paper 

VISTA+: Mesh-based single-view novel view synthesis and CAD-based foreground composition for synthetic driving-scene generation. Please check the readme.md in uploaded world2mesh2.zip to check how to use it.

The dataset ID has been included in the final version of the manuscript.

This replication package provides the aggregated literature data supporting the main descriptive analyses presented in the manuscript. As this study is a literature review, it does not involve primary empirical data collection, experimental investigations, or algorithmic simulations. The accompanying Excel file contains the compiled data used to analyze publication trends, research application domains, and flight data types. All reviewed studies are cited in the manuscript to ensure traceability of the literature sources. These materials support the verification and reproduction of the main descriptive statistical findings reported in the study.

Replication package for COMMTR-2026-0064.R2 ("Agent-Pilot: A Visual Hybrid Intelligence Closed-Loop Framework for Supervised Autonomous eVTOL Landing"). Completed: 160 landing trials (16 scenarios x 10), the 2,240-case vision-language safety benchmark with ground truth, 1,600 input images, two full prediction sets and metrics, 400 API-versus-local latency pairs, 65 human-pilot records, the raw OFAT PID sweep (13 configurations x 10 repetitions per axis), and twelve flights' telemetry with 221 board-corner annotations and 137 masks. All source code and the simulator-side MSFS 2024 developer kit are included. Run python repro/run_all.py to recompute 226 manuscript cells. The Replication Explanatory File is included. Raw videos (6.4 GiB) and the 1,600 lossless PNGs (3.6 GiB) exceed the 3 GB limit and are hosted at https://huggingface.co/datasets/yuxindu/agent-pilot-open with checksums in MEDIA_EXCLUDED.md. License: MIT; third-party components keep Apache-2.0.

This replication package supports the manuscript "Driving risk emerges from the required two-dimensional joint evasive acceleration", submitted to Communications in Transportation Research. It provides processed risk-measure data, analysis and plotting code, reference results, validation records and a Replication Explanatory File. Source cohorts comprise 658 reconstructed crashes from CIMSS-TA and 44,180 noncrash interactions from highD, exiD, inD, SinD and Argo2-LCR. Primary warning analyses use the [-3.0, -0.1] s window with zero terminal tolerance; full-history analyses with 0.1 s tolerance are a sensitivity check. The package covers the principal discrimination, warning and information statistics in Figures 3-6 and Tables 1-4. Raw source trajectories and crash recordings are not redistributed. Original dataset access and reuse conditions remain applicable. The README describes the data, software requirements and reproduction steps.

This replication package accompanies the manuscript “Compatibility-Invariance Conflict: a CLF-CBF surrogate safety measure for vehicle trajectories,” submitted to the Communications in Transportation Research under manuscript ID COMMTR-2026-0264. The code accompanying this manuscript is provided with the scripts, environment specification and instructions required to reproduce the reported analyses after the relevant source data are obtained under their original access conditions. SHRP2 reconstructed trajectories are publicly available from the source record at `https://doi.org/10.15787/VTT1/T7UUC1`. The reconstructed 100-Car files and source-data information are available from `https://github.com/Yiru-Jiao/Reconstruct100CarNDSData` and `https://doi.org/10.15787/VTT1/CEU6RB`. CIDAS source data cannot be publicly redistributed; authorized users must obtain the three documented cases through the applicable provider or institutional process.  The data/examples/ directory contains small public-derived fragments for loader validation only and does not​ satisfy the full input contract.

This code-only replication package corresponds to the revised manuscript “Vision Language Model-Guided Semantic Distillation Network for Pedestrian Crossing Intention Prediction” submitted to Communications in Transportation Research (Manuscript ID: COMMTR-2026-0077).

The package contains the implementation of the proposed VLM-GSDN framework, including scripts for generating pedestrian-level and scene-level semantic descriptions using Qwen, InternVL, MiniCPM, and Phi models, as well as training and evaluation workflows for the main model and different VLM-GSDN variants on the JAAD and PIE datasets. It also provides configuration files, environment specifications, and supporting documentation. A Replication Explanatory File describes the package structure, data preparation, model training, and evaluation procedures.

This package contains the numerical propagated interaction-field model, context-conditioned physics-informed neural network (PINN), field-aware reinforcement-learning pipeline, and processed measurements supporting the principal validation and simulator findings. 

The statistical workflow preserves the original observation/reward factorial and replaces only its complete-model merge candidate with the selected 0.25-times comfort profile. All five calibrated merging Soft Actor-Critic checkpoints and their five paired stock checkpoints are included. Policy-training seeds are 0-4; each policy is evaluated on traffic seeds 10000-10049 at density 0.3. The calibrated profile uses jerk weight 0.000375 and steering/throttle-change weights 0.00025. An indexed mapping connects each checkpoint to its effective training settings and episode CSV. 

The PINN checkpoint is accompanied by its available architecture, input channels, normalization, numerical-solver settings, loss weights, calibration-cache signatures, hardware record, training duration and 6,000-step loss history. 

This replication package accompanies the manuscript “CF-Drive: Candidate-conditioned interaction risk estimation for retrieval-augmented autonomous driving,” submitted to the Journal of Intelligent and Connected Vehicles under manuscript ID JICV-2026-0117. The package contains the source code, experimental configurations, log-disjoint fold definitions, processed candidate-level results, evaluation and plotting scripts, and a Replication Explanatory File for reproducing the principal risk-estimation, matched-shuffle, and utility-selection results. The original NAVSIM data are not redistributed. Users should obtain the source data from the official NAVSIM repository and comply with its applicable terms of use.

This replication package accompanies the manuscript "Hierarchical Neural-Scope Detector for Silent Fault Detection in Vision-Language-Action-Based Autonomous Driving," submitted to Communications in Transportation Research.

The package contains experimental code, processed data, detector models, feature specifications, evaluation configurations, sample-level predictions, and supporting results. The materials are organized into five modules covering OpenDriveVLA detector development, UniAD2 cross-architecture validation, independent validation, component ablation and threshold-sensitivity analysis, and runtime overhead evaluation.

These materials correspond to the experiments and results reported in the main manuscript, appendices, and supplementary information. The package includes README files and a replication guide describing the data, code, and procedures for reproducing the reported results.

The materials are also available at https://github.com/xlqy-114/Hierarchical-Neural-Scope-Detector.

OVPD is a virtual-physical fusion testing dataset derived from the 2025 OnSite Autonomous Driving Challenge. It contains 20 complete clip-level test runs from 20 teams, covering nearly 3 hours of driving data and 15 atomic scenarios for emergency response, traffic efficiency, and rule compliance.

This package includes structured data synchronized at 10 Hz: ego-vehicle trajectories and motion states, control commands, background traffic trajectories and states, traffic-light states, OpenDRIVE HD maps, and evaluation results for safety, efficiency, comfort, rule compliance, and traffic coordination. It supports replay-based analysis, planning and decision-making research, failure diagnosis, and capability-oriented benchmarking.

Due to the platform’s 3 GB upload limit, image data are not included here. The complete OVPD dataset, including synchronized image data, is available at: https://huggingface.co/datasets/Yuhang253820/Onsite_OVPD