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Work zone speed is one of the most important factors in road construction safety management. This work presents a computer vision based technique designed to measure lane-specific individual vehicle speed using existing traffic monitoring cameras and computers. The resulted speeds support the influence analysis of factors including traffic control, lane positions, and construction activity. Object detection (YOLOv5) and tracking (Deep-SORT) algorithms are combined to track the vehicles. In particular, 21 days’ worth of road construction videos are collected from a pole-mounted traffic monitoring camera operated by the Texas A&M University Transportation Services. Based on the object detection results, a novel construction activity inference technique is developed to approximate the times when construction workers are present. Based on this time separation, the vehicle speeds with and without the presence of construction activity are compared. The proposed framework is able to measure speeds with an error ranging from 0 to 6.4 kilometers per hour (KPH). Detailed analysis of this video data suggests that traffic control with barrels in the median work zone lowers the average speed (for all vehicles) by 15 KPH. The lane adjacent to the work zone also has higher speed variation than the other lanes. The construction activity speed comparisons show when the traffic is slow (possibly traffic after a red light), the difference is statistically significant with a p-value ranging from 0.01 to 0.03. When the traffic is fast (possibly traffic encountering a green signal as they approached the nearby intersection) construction activity has no significant effect on the work zone speeds. The proposed CV technique is a reliable and cheap method to measure lane-specific work zone speeds. The derived measurements support detailed safety analysis. Other than work zone speeds, the proposed technique can also be used for regular traffic speed monitoring.

The evaluation of the road surface roughness condition plays an important role for the assessment of the pavement condition. To measure the international roughness index (IRI), new emerging surveying techniques can be used in addition to the profilometer. Mobile laser scanner (MLS) data were acquired on a stretch of road and interpolated on a grid to build a 3D model representing the whole road surface. To evaluate the roughness, both dynamic and geometric IRIs were computed from (digital elevation model) DEM-derived elevation attributes. The computed values showed a similar trend and a high correlation with that derived from the profilometer; the differences between the corresponding values were generally less than 5%. To evaluate the effects of vibrations on human comfort, the built DEM was integrated into a dynamic simulation software user interface to compute the effects on the drivers of the vertical accelerations, considered another indicator of the pavement performance.