## 1 Introduction  
The design and operation of haulage systems are very important in mining sites. This is because mining costs may vary significantly according to the design and operation methods [1]. Various simulation techniques have been used for haulage systems in mines [2; 3; 4; 5; 6; 7; 8]. In the case of a mine truck haulage system, simulations may be used to predict productivity indices, such as the mine production and equipment utilization, according to system operating conditions, including the task time, amount of equipment, dispatch interval, and time consumed per unit task [9; 10]. In this manner, it is possible to select optimal equipment combinations [11; 12; 13; 14], establish equipment dispatch plans [15; 16; 17; 18; 10], and identify optimal haulage routes [19; 20; 21; 22]. The numerous simulation techniques that have been developed thus far are based on algorithms that consider the sequential tasks performed by trucks. However, algorithm-based simulation techniques have certain problems. Mines are dynamic systems and hence unexpected events often occur, such as failure of mining equipment and crusher capacity overflow. Accurate simulations require humans to design and develop haulage system logic, taking into account all events that may occur in a mining environment. In addition, it is necessary to modify existing algorithms to consider a new loading point or haulage road changes and several algorithms should be adapted to simulate real-time dispatching truck haulage systems. To address these problems, technologies such as sensor networks, mobile devices, the Internet of things, and cloud computing have been introduced into mining sites, and researchers have been studying simulation techniques using big data collected on-site rather than algorithms based on prior knowledge [23]. Moreover, deep learning has been attracting attention as a technology that efficiently analyzes big data according to its purpose [24; 25]. Deep learning is an artificial intelligence technology that learns object characteristics hierarchically, without human interference, and improves the prediction performance by means of error evaluation [26]. Deep learning was developed based on the deep neural network (DNN), which is a supervised machine-learning technique [27]. DNNs imitate the neurons (nodes) and synapses that connect the neurons within the human brain. They have a multilayer perceptron structure in which an input layer, output layer, and one or more hidden layers are connected hierarchically [28; 29; 30]. The nodes of each layer are connected to all nodes on the next layer, and the output values are determined through a feed-forward network [31]. Various deep learning models based on the DNN have been developed by numerous researchers [32]. Convolutional neural networks (CNNs) are used to learn two-dimensional pixel data, such as images and videos [33]. A CNN is composed of a convolutional layer, pooling layer, and fully connected layer. It calculates the sum of weights between the training data and filter (or kernel) to extract detailed characteristics from images [34]. A recurrent neural network (RNN) is a model suitable for learning time series data that change sequentially over time [35]. It is mainly used to save the input elements of the previous time stage in the neural network and to predict the value of the next time stage [36]. Furthermore, auto-encoders are models that continuously perform encoding and decoding tasks to analyze the characteristics of input values [37; 38]. Deep belief networks are stochastic models that learn the stochastic distribution of input data in the form of stacks of restricted Boltzmann machines, in which the visible and hidden layers are connected bidirectionally [39; 40]. In recent years, researchers have published reports on the use of deep learning in the mining industry. Deep learning technology has been applied in the exploration stage to detect anomalies [41; 42; 43], analyze deposit potential [44; 45; 46; 47], and classify downhole exploration data [48].