The Effects of Inaccurate Traffic Information on Driver Behavior and Acceptance of an Advanced In-Vehicle Traveler Information System

Hanowski, RJ; Kantowitz, BH; Kantowitz, SC · 1998 · ROSA P / United States. Department of Transportation. Federal Highway Administration, Turner-Fairbank Highway Research Center

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Summary

This study investigates the threshold of traffic information accuracy required for drivers to trust and accept advice from Advanced Traveler Information Systems (ATIS). Motivated by the need for human factors design guidelines for Intelligent Transportation Systems, the research addresses how system reliability impacts driver performance and acceptance, particularly when information is imperfect due to network noise or congestion. The study specifically examines how information accuracy and driver familiarity with the traffic network interact to influence route selection and system trust. The researchers employed the Battelle Route Guidance Simulator to test 48 drivers across four trials. The experimental design manipulated two primary variables: information accuracy (100%, 71%, or 43%) and network familiarity (a familiar real-world network in Seattle versus an unfamiliar artificial network topologically matched to Seattle). Drivers navigated routes by querying the system for traffic congestion data before selecting links. Objective measures included penalty costs for non-optimal routes, convergence with pre-planned routes, and system query frequency. Subjective measures assessed driver trust, self-confidence, and traffic expectations. Results indicated that while 100% accurate information yielded the best performance and highest trust, information that was 71% accurate remained acceptable and useful to drivers. However, accuracy dropping to 43% caused significant decrements in both performance and subjective opinion, leading drivers to prefer their own judgment over the system’s advice. Drivers utilized accurate ATIS information less effectively in familiar settings compared to unfamiliar ones, resulting in higher penalty costs in familiar environments. This suggests that drivers rely more on internal mental models in familiar areas, making them more critical of system advice. Additionally, driver trust decreased following inaccurate information but recovered when subsequent information was accurate, though recovery was less complete in the 43% accuracy condition. The findings imply that ATIS designers should aim for information accuracy above 71% to maintain user acceptance. The study concludes that commercial success for in-vehicle ATIS may be easier to achieve in unfamiliar settings, such as rental vehicles for visitors, because drivers have lower self-confidence and are more willing to accept automated advice. Conversely, systems intended for private use in familiar home cities must meet higher standards of reliability to overcome drivers' reliance on their own knowledge. These results provide empirical data for establishing reliability thresholds that balance system imperfections with driver trust.

Key finding

Information accuracy above 71 percent is recommended for system designers, as 43 percent accuracy significantly degrades driver performance and trust, while 71 percent accuracy remains acceptable.

Methodology

simulator

Sample size: 48

Provenance

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

Topics

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• trust calibration
• situational awareness
• mental model of traffic

Information type

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• Empirical Findings: self report data
• Methodological Resource: validation psychometrics
• Theoretical Contribution: computational model