Replication Data for: Evaluation of Autonomous Vehicles and Smart Technologies for Their Impact on Traffic Safety and Traffic Congestion [supporting datasets]

Miles, James; Strybel, Thomas · 2020 · ROSA P / California. Dept. of Transportation. Division of Research and Innovation

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Summary

This document serves as a metadata record and replication data package for the study titled "Evaluation of Autonomous Vehicles and Smart Technologies for Their Impact on Traffic Safety and Traffic Congestion." Funded by the U.S. Department of Transportation and preserved by the Pacific Southwest Region University Transportation Center, the dataset is hosted in the Harvard Dataverse repository. The primary purpose of this resource is to provide open access to the raw data generated during the research, enabling verification and further analysis of the findings presented in the associated final report available through the National Transportation Library’s Digital Repository. The underlying research involved collecting demographic, survey, and behavioral data from 36 participants, predominantly undergraduate and graduate students at California State University Long Beach. All identifying information was removed to ensure participant anonymity. The data collection focused on driver performance within a simulated highway environment, specifically examining obstacle avoidance maneuvers. The dataset comprises three comma-separated value (CSV) files: one containing demographic and trust questionnaire data, one detailing driver performance metrics during obstacle maneuvers, and one recording workload measures. The data includes specific subjective and objective measures. Subjective reports of participant trust in automation were adopted from Jian et al. (2000) using a six-choice Likert-type scale, designed to measure attitudes toward automation, including specific trust items and overall views. Workload was assessed using the NASA Task Load Index (NASA-TLX), a paper-based scale measuring six dimensions: Mental Demands, Physical Demands, Temporal Demands, Frustration, Effort, and Performance. A single workload score was calculated for each track by averaging the percent ratings (0–100) across all dimensions, with higher values indicating increased subjective mental workload. Objective data includes vehicle state measurements at the initiation and completion of each obstacle avoidance maneuver. This dataset supports the broader field of transportation research by providing transparent access to the empirical evidence regarding human factors in autonomous vehicle interactions. By making demographic, trust, workload, and performance data publicly available, the resource facilitates replication studies and secondary analyses of how smart technologies impact traffic safety and congestion. The National Transportation Library notes that no additional curation was performed on the dataset, as it is preserved in an external repository compliant with U.S. DOT public access plans. Researchers are directed to the Harvard Dataverse for access and to the NTL data curator for assistance if access issues arise.

Methodology

dataset

Sample size: 36

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (7 acquisition events logged).

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

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• traffic density
• acceptance adoption

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: self report data
• Methodological Resource: dataset resource, tool software