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Continuous condition monitoring and inspection of traffic signs are essential to ensure that safety and performance criteria are met. The use of 3D point cloud modeling by the construction industry has been significantly increased in recent years especially for recording the as-is conditions of facilities. The high-precision and dense 3D point clouds generated by photogrammetry can facilitate the process of asset condition assessment. This paper presents an automated computer-vision based method that detects, classifies, and localizes traffic signs via street-level image-based 3D point cloud models. The proposed pipeline integrates 3D object detection algorithm. An improved Structure-from-Motion (SfM) procedure is developed to create a 3D point cloud of roadway assets from the street level imagery. In order to assist with accurate 3D recognition and localization by color and texture features extraction, an automated process of point cloud cleaning and noise removal is proposed. Using camera pose information from SfM, the points within the bounding box of detected traffic signs are then projected into the cleaned point cloud by using the triangulation method (linear and non-linear) and the 3D points corresponding to the traffic sign in question are labeled and visualized in 3D. The proposed framework is validated using real-life data, which represent the most common types of traffic signs. The robustness of the proposed pipeline is evaluated by analyzing the accuracy in detection of traffic signs as well as the accuracy in localization in 3D point cloud model. The results promise to better and more accurate visualize the location of the traffic signs with respect to other roadway assets in 3D environment. [ABSTRACT FROM AUTHOR]

The visibility of a traffic sign at night depends on its retro-reflectivity, a property that needs to be frequently monitored to ensure transportation safety. In the U.S., Federal Highway Administration (FHWA) maintains regulations to ensure minimum retro-reflectivity levels. Current measurement techniques either (a) use vehicle mounted device during the night, or (b) use manual handheld devices during the day. The former is expensive due to nighttime labor cost. The latter is time-consuming and unsafe. To address these limitations, this paper presents a computer vision-based technique to measure retro-reflectivity during daytime using a vehicle mounted device. The presented algorithms simulate nighttime visibility of traffic signs from images taken during daytime and measure their retro-reflectivity. The technique is faster, cheaper, and safer as it neither requires nighttime operation nor requires manual sign inspection. It also satisfies FHWA measurement guidelines both in terms of granularity and accuracy. The performance of the presented technique is evaluated under various testing conditions. The results are promising and demonstrate a strong potential in lowering inspection cost and improving safety in practical applications on retro-reflectivity measurement. [ABSTRACT FROM AUTHOR]

Conflicts inevitably occur in major transport projects as various stakeholders express diverse, often conflicting needs and concerns, and failing to address and manage these conflicts often leads to project failures. Understanding stakeholders' perceptions and the discrepancies among them is crucial to an effective dialogue among the parties seeking to build consensus. This study investigates the potential influences of stakeholder characteristics as well as project environments on conflicts and consensuses in stakeholders' overall preferences about sustainable transport projects. Data on the perspectives of three stakeholder-group types (system providers, project designers, and system users) with regard to 14 sustainability criteria are drawn from a survey of eight major transport projects across China. Two-way analysis of variance is then carried out to investigate whether or not the differences in the mean scores among stakeholder-group types and cities were statistically significant. The results show that discrepancies of opinion about transport-project sustainability criteria prevail among stakeholder-group types in every region we studied, due to these types' different needs and concerns. Our findings also suggest that special attention should be given to cases in which multiple stakeholder groups assign the same priorities to certain criteria, as those criteria are likely to represent the most serious issues affecting all stakeholders in a given project environment. With regard to the potential influences of project environments, we found that, in general, the discrepancies in stakeholders' prior concerns about sustainability criteria exhibited no obvious geographic pattern. However, exceptions did occur when a specific criterion was of major concern in a given project environment. Taken together, our findings emphasize the necessity of addressing and managing specific local issues, and the divergent concerns of stakeholders, when planning sustainable-transport projects. [ABSTRACT FROM AUTHOR]