Choice of speed under compromised Dynamic Message ...

Socioeconomic and attitude correlates

Looking at Scenario 1, it is found that drivers with higher trust in DMS, those who pay more attention to the traffic-related information, and those with a disability are likely to comply the fabricated-realistic information. However, white participants and those with long weekly commute hours are more likely to ignore the fabricated-realistic information. All significant explanatory variables have a negative correlation with increased speed, indicating that participants who are familiar with DMS are unlikely to speed up. We found female drivers, drivers with high trust in DMS, and tech-friendly drivers are likely to slow down in Scenario 1. The results also show that African-American drivers, drivers with a lack of sense of direction, and drivers with careful driving style are likely to stop.

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Similar to Scenario 1, all variables except &#;Getting Bored&#; has a negative correlation with the do-nothing choice in Scenario 2. The results indicate that older drivers, drivers with lower education, and drivers with more attention to DMS are likely to comply the fabricated-realistic information. However, if the respondent finds driving a tedious activity, the chance of ignoring the fabricated-realistic information is higher for the respondent. We found female, white, and high-income drivers are unlikely to speed up in Scenario 2, while reckless and careless drivers are likely to speed up. The results also demonstrate that female drivers, drivers in rural areas, and drivers with a lack of sense of directions are likely to slow down. If the respondent was an African-Americans, males, and safe drivers have a higher chance of stopping in Scenario 2 than other groups.

Looking at Scenario 3, it is found that the tendency to slow down or stop is statistically more significant in our sample than do nothing or speed up. The results demonstrate that white drivers, drivers with long weekly commute hours, and drivers with DMS familiarity are likely to ignore Scenario 3. Compared with other speed choices (i.e., ignore, slow down, stop), participants with more driving experience, and those who read the content of DMS with more frequency are less likely to speed up under Scenario 3. All, but the &#;DMS_Read&#; variable were found to have a positive correlation to slowing down under Scenario 3. Drivers of a single unit truck, anxious drivers, single, and older drivers are more likely to slow down in Scenario 3. We also found drivers who read DMS more often in their commute are less likely to slow down. The results indicate that reckless and careless drivers, those who live in urban areas, and those who ride a motorcycle for most of their commute are more likely to stop under Scenario 3. Previous involvement in an accident and high trust in the DMS are factors that inhibit subjects from stopping when the DMS disseminate fictitious message.

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The result indicates that participant reaction to the Scenario 4 is fairly similar to Scenario 4. Participants with more than 20 years of driving experience and those who read DMS content in their daily commute are more likely to ignore the fabricated information. However, young drivers between 25 and 34 years show a negative attitude toward the do-nothing choice. Under Scenario 4, highly educated drivers and those who have up to five years of driving experience have a higher tendency to slow down under Scenario 4. Three variables which have a negative association with the speeding up in Scenario 4 are &#;White,&#;&#;High Income,&#; and &#;Female.&#; Living in urban areas and having relatively low driving experience are also factors that contribute to the higher chance of stopping under Scenario 4. The slow down and stopping behavior could rise simply from subjects&#; interests in abnormal and infrequent messages. Nonetheless, the sign would cause unexpected slowdown and stopping behavior with the potential to create unsafe traffic patterns.

Here, we explore the correlation between speed behavior in and within four scenarios. depicts the correlation matrix. As shown, there is a strong correlation between similar choices of speed when comparing S1 and S2 with S3 and S4. For example, as indicated in , drivers perceiving a slow down or stop are highly correlated under S1 and S2 or S3 and S4, showing similar speed variation behavior within scenarios under realistic or fictitious content.

The correlation estimates are not strong when assessing the results across the realistic and fictitious scenarios. Results show drivers conflicting attitudes toward the realistic scenarios compared to fictitious scenarios. In addition, among the four choices of speed, the do-nothing behavior indicates negative correlation across realistic and fictitious scenarios. In spite of these differences, drivers are corroborating similar patterns across all scenarios. This means speeding up, slowing down, and stopping is positively correlated across all scenarios. It is noteworthy to mention that the slowdown choice has the lowest positive correlation compared to speed up and stopping.

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