Lane-specific speed analysis in urban work zones with computer vision.

OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Visual recognition for urban traffic data retrieval and analysis in major events using convolutional neural networks.

Accurate and prompt traffic data are necessary for the successful management of major events. Computer vision techniques, such as convolutional neural network (CNN) applied on video monitoring data, can provide a cost-efficient and timely alternative to traditional data collection and analysis methods. This paper presents a framework designed to take videos as input and output traffic volume counts and intersection turning patterns. This framework comprises a CNN model and an object tracking algorithm to detect and track vehicles in the camera's pixel view first. Homographic projection then maps vehicle spatial-temporal information (including unique ID, location, and timestamp) onto an orthogonal real-scale map, from which the traffic counts and turns are computed. Several video data are manually labeled and compared with the framework output. The following results show a robust traffic volume count accuracy up to 96.91%. Moreover, this work investigates the performance influencing factors including lighting condition (over a 24-h-period), pixel size, and camera angle. Based on the analysis, it is suggested to place cameras such that detection pixel size is above 2343 and the view angle is below 22°, for more accurate counts. Next, previous and current traffic reports after Texas A&M home football games are compared with the framework output. Results suggest that the proposed framework is able to reproduce traffic volume change trends for different traffic directions. Lastly, this work also contributes a new intersection turning pattern, i.e., counts for each ingress-egress edge pair, with its optimization technique which result in an accuracy between 43% and 72%.