Abstract
Air traffic control is an important tool to ensure the safety of civil aviation. For the departments that do the work of air traffic control, reducing the percentage of unsafe event is the core task of safety management. If the relationship between the percentage of unsafe event and their influencing factors can be effectively clarified, then the probability of unsafe event in some control department can be predicted. So, it is of great importance to improve the level of safety management. To quantitatively estimate the probability of unsafe event, a three-layer BP neural network model is introduced in this paper. First, a probabilistic representation of unsafe event related to air traffic control department is made, and then, the probability of different classes of unsafe events and safe events is taken as the outputs of the BP neural network, the factors influencing occurrence of unsafe event connected with air traffic control is taken as inputs, and the sigmoid function is chosen as activation function for the hidden layer. Based on the error function of neural network, it is proved that the general BP neural network has two drawbacks when used for the training of small probability events, which are as follows: the pattern does not ensure that the sum of probability of all events is equal to one and the relative error between the actual outputs and desired outputs is very large after the training of neural network. The reason proved in this paper is that the occurrence rate of the unsafe event is much smaller than that of the safe event, resulting in each weight in the hide layer being subjected to the desired outputs of the safe event when using the gradient descent method for network training. To address this issue, a new mapping method is put forward to reduce the large difference of the desired outputs between the safe event and unsafe event. It is theoretically proved that the mapping method proposed in this paper can not only improve the training accuracy but also ensure that the sum of probability is equal to one. Finally, a numeric example is given to demonstrate that the method proposed in this paper is effective and feasible.
Highlights
China has a large population, vast geographical area, and uneven distribution of natural resources
It is obtained by feeding the inputs into the trained network and is transformed by reverse normalization in improved BP neural network and normalized BP neural network. e(·) represents the absolute error between the actual outputs and desired outputs, and Re(·) represents the relative error between actual outputs and desired outputs. p(A) is the sum of the probability of different event, which is used to check whether the normalization of probability is satisfied. e calculation formula of each parameter in the tables is as follows: e An p An − t An, Re e An t p(A) p A1 + p A2, where A1 stands for safe event, and A2 stands for unsafe event
E analysis result of the general BP neural network is shown in Table 4. e analysis result of the normalized BP neural network is shown in Table 5. e analysis result of the improved BP neural network is shown in Table 3: (1) From Table 3, it is easy to see that the sum of the actual outputs used as the probability of air traffic control (ATC) event is equal to one in the improved BP neural network
Summary
China has a large population, vast geographical area, and uneven distribution of natural resources. To promote economic development and improve people’s life, the exchange of people and goods among different regions is very frequent. Due to the large population and uneven distribution of the population in China, the transportation demands are diverse, and to meet different people’s demand, the Chinese government has been committed to building a diversified comprehensive transportation system, creating a comprehensive transportation network integrating railroads, highways, waterways, and civil aviation [1,2,3,4,5]. In 2019, China civil aviation completed a total of 129.27 billion ton-kms of freight turnover, 660 million person-times of passengers, and 752.6 million tons of cargo and mail, with growth rates of 7.1%, 7.9%, and 1.9% year-on-year, respectively. With the advantages of safety, speedability, and convenience, civil aviation is winning more and more people’s choice, and the share of civil
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