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.