Computer vision-based approach for smart traffic condition assessment at the railroad grade crossing

Slow-moving or stopped trains at highway-railroad grade crossings, especially in the populated metropolitan areas, not only cause significant traffic delays to commuters, but also prevent first responders from timely responding to emergencies. In this study, the researchers introduce an automated video analysis, detection and tracking system to evaluate the traffic conditions, analyze blocked vehicle behaviors at grade crossings, and predict the decongestion time under a simplified scenario. A novel YOLOv3-SPP+ model has been developed to improve the detection performance with dividing the image from finer to coarser levels and enhance local features. The SORT module has been integrated to the model for a simple yet efficient manner to track vehicles at the railroad grade crossing. Two field datasets at the Columbia, SC, with train blockage video records have been tested. The model training performance has been evaluated by mAP @0.5, F1 score, and total loss. Based on the training results, our model outperforms other YOLO series models. The field tracking performance has been assessed by the ratio between prediction and ground truth. The mean value of accuracy of our test cases is 92.37%, indicating a reliable tracking performance. In addition, the present results indicate the traffic during and after the crossing blockage does follow a pattern, and there is a general trend of the behavior of the vehicles waiting or taking an alternative route. A good linear correlation between the decongestion time and the number of blocked vehicles has been observed at the monitored grade crossing at the City of Columbia, SC.

Automated summary

In this study, researchers introduced an automated video analysis, detection and tracking system to assess traffic conditions, analyze behaviors of blocked vehicles at grade crossings, and predict decongestion time under a simplified scenario. A new YOLOv3-SPP+ model was developed to improve detection performance by dividing images into finer to coarser levels and enhancing local features. The results indicated a pattern in vehicle behavior during and after crossing blockages, with a good linear correlation between decongestion time and the number of blocked vehicles.