Computer vision-based approach for smart traffic condition assessment at the railroad grade crossing |
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| Affiliation: | 1. Dept. of Civil and Environmental Engineering, University of South Carolina, Columbia, SC 29208, USA;2. Dept. of Mechanical Engineering, University of South Carolina, Columbia, SC 29208, USA;1. Key Laboratory of Industrial Engineering and Intelligent Manufacturing, School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, Shaanxi, China;2. State Key Laboratory of Intelligent Manufacturing System Technology, Beijing 100854, China;3. Innovation Center for Liquid Rocket Engine Digital Research and Development, CNSA;1. Dept. Architectural Engineering, Dankook Univ, 152 Jukjeon-ro, Suji-gu, Yongin-si, Gyeonggi-do, 16890, South Korea;2. Dept. of Architectural Engineering, Namseoul, Univ, 91, Daehak-ro, Seonghwan-eup, Seobuk-gu, Cheonan-si, Chungcheongnam-do, 31020, South Korea;3. Dept. Architectural Engineering, Dankook Univ, 152 Jukjeon-ro, Suji-gu, Yongin-si, Gyeonggi-do, 16890, South Korea;1. College of Management, Shenzhen University, Shenzheng 518073, China;2. Commercial College, Xi’an International University, Xi’an 710077, China;3. CCCC Third Harbor Consultants Co., Ltd., Shanghai 200032, China;4. Institute of Transportation Studies, University of California Davis, Davis, CA 95616, USA;1. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, PR China;2. Laboratory of Science and Technology on Integrated Logistics Support, National University of Defense Technology, Changsha 410082, PR China |
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| Abstract: | 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. |
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| Keywords: | Grade crossing Traffic assessment Computer vision Deep learning Traffic delay |
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