@article{ref1,
title="Development of computer vision informed container crane operator alarm methods",
journal="Transportmetrica A: transport science",
year="2024",
author="Yan, Ran and Tian, Xuecheng and Wang, Shuaian and Peng, Chuansheng",
volume="20",
number="2",
pages="e2145862-e2145862",
abstract="To reduce the extra work, the operation cost, and the risk of cargo delay induced by the unloading of wrong containers, this study first develops a container color detection model to predict the color of the container being unloaded. The prediction results are then used to develop two crane operator alarm methods. <br><br>METHOD 1 alerts the crane operator if the detected color of a container is not in compliance with the correct container color. <br><br>METHOD 2 constructs a decision problem to decide whether to alert the operator. The results of numerical experiments show that methods 1 and 2 are better than the benchmark. Specifically, method 1 can save the expected annual total cost by about 82% while method 2 can save the expected annual total cost by about 85%. Extensive sensitivity analysis is also conducted to verify the methods performance and robustness.<p /> <p>Language: en</p>",
language="en",
issn="2324-9935",
doi="10.1080/23249935.2022.2145862",
url="http://dx.doi.org/10.1080/23249935.2022.2145862"
}