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Abstract
Safety is the key point of railway transportation, and railway traffic accident prediction is the main content of safety management. There are complex nonlinear relationships between an accident and its relevant indexes. For this reason, triangular gray relational analysis (TGRA) is used for obtaining the indexes related to the accident and the deep auto-encoder (DAE) for finding out the complex relationships between them and then predicting the accident. In addition, a nonlinear weight changing particle swarm optimization algorithm, which has better convergence and global searching ability, is proposed to obtain better DAE structure and parameters, including the number of hidden layers, the number of neurons at each hidden layer and learning rates. The model was used to forecast railway traffic accidents at Shenyang Railway Bureau, Guangzhou Railway Corporation, and Nanchang Railway Bureau. The results of the experiments show that the proposed model achieves the best performance for predicting railway traffic accidents.
Highlights
An accident affecting normal train operation is called a railway traffic accident, such as conflict, derailment, fire, or explosion
This paper mainly provides the following three innovations: (i) deep auto-encoder (DAE) is applied to predict railway traffic accidents. (ii) The improved particle swarm optimization (PSO) (IPSO) is used for DAE to decide the number of hidden layers, the number of neurons of each hidden layer, the learning rate of each hidden layer when reconstructing input data during pre-training and the learning rate of back-propagation algorithm during fine-tuning. (iii) In order to find the appropriate number of layers and neurons of hidden layers quickly, IPSO is proposed, which features good global search ability and fast convergence speed
Data includes railway traffic accidents recorded at Shenyang Railway Bureaus, Guangzhou Railway Corporation, and Nanchang Railway Bureau from 1999 to 2013
Summary
An accident affecting normal train operation is called a railway traffic accident, such as conflict, derailment, fire, or explosion. Railway is the main artery of Chinese economy related to production development, standard of living, and social welfare. It occupies a very important position in Chinese transportation systems. Given that railway transportation is characterized by high speed, high density, and heavy loads, traffic security is facing new demands and challenges. Transportation enterprises must compensate for losses incurred when goods are lost, short, deteriorated, contaminated, or damaged [1]. Accurate prediction of railway traffic accidents plays a crucial role in railway safety warning systems and reduces the losses of transportation enterprises
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