A critical deficiency in any one or a combination of three transportation system characteristics: the driver, roadway, or vehicle can contribute to an elevated crash risk for the motoring public. Traffic signs often convey critical information to drivers. However, traffic signs are only effective when clearly visible and legible. Traffic sign vandalism that is exclusively the results of humans causes both sign legibility and visibility to deteriorate. Transportation agencies spend a significant amount of money to repair or replace vandalized signs. This study was conducted to identify which traffic signs are more vulnerable to vandalism. To do this, a mobile-based vehicle collected data of over 97000 traffic signs managed by the Utah Department of Transportation (UDoT), US. The vandalized signs were identified by a trained operator through inspection of daytime digital images taken of each individual sign. Location data obtained from online sources combined with the traffic sign data were imported into ArcGIS to acquire localized conditions for each individual sign. According to the chi-square test results, the association between vandalism and traffic sign attributes and localized conditions, including background color, size, mount height, exposure, land cover, and road type was evident. After employing the random forests model, the most important factors in making signs vulnerable to vandalism were identified.
transportation infrastructure, traffic sign vandalism, mobile-based data collection, mobile LiDAR, digital imaging, geographic information system, random forests
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