Abstract
This study develops an algorithm to detect the risk of collision between trucks (i.e., yard tractors) and pedestrians (i.e., workers) in the connected environment of the port. The algorithm consists of linear regression-based movable coordinate predictions and vertical distance and angle judgments considering the moving characteristics of objects. Time-to-collision for port workers (TTCP) is developed to reflect the characteristics of the port using the predictive coordinates. This study assumes the connected environment in which yard tractors and workers can share coordinates of each object in real time using the Internet of Things (IoT) network. By utilizing microtraffic simulations, a port network is implemented, and the algorithm is verified using data from simulated workers and yard trucks in the connected environment. The risk detection algorithm is validated using confusion matrix. Validation results show that the true-positive rate (TPR) is 61.5∼98.0%, the false-positive rate (FPR) is 79.6∼85.9%, and the accuracy is 72.2∼88.8%. This result implies that the metric scores improve as the data collection cycle increases. This is expected to be useful for sustainable transportation industry sites, particularly IoT-based safety management plans, designed to ensure the safety of pedestrians from crash risk by heavy vehicles (such as yard tractors).
Highlights
Technologies, educational programs, and policies designed to prevent industrial accidents have been widely implemented, and accidents are on the decline in various industrial sectors
As a criterion for determining the accuracy of the predicted value, the above two situations were set as the actual class (Table 2). e confusion matrix is used to compare the number of true and false predicted values and actual values. e simulation results of algorithm are classified into four categories to evaluate detection accuracy as follows: True positive (TP): the algorithm predicted that a situation is dangerous, and it is true
False positive (FP): the algorithm predicted that a situation is dangerous, and it is false
Summary
Technologies, educational programs, and policies designed to prevent industrial accidents have been widely implemented, and accidents are on the decline in various industrial sectors. TTC is used in detection algorithms as an indicator of real-time collisions, and in assessing safety levels based on trajectory [14]. The Wu et al study developed collision-prevention algorithms by processing real-time data that can be used in connected vehicle environments [18].
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