Abstract
The accurate and rapid prediction of ticket prices for a public-private partnership (PPP) subway system, which is an important research topic in the field of civil engineering management, is of critical importance to ensure its smooth operation. To effectively cope with the effects of multiple influencing factors and strong nonlinearity among them, the mean impact value (MIV) method and the back-propagation (BP) feed-forward neural network improved by the sparrow search algorithm (SSA) are used in this study to develop an intelligent prediction model. First, we considered the relationship of the supply and the subway system service, which is a typical quasi-public product, and analyzed the relevant factors affecting its price adjustment. Then, we developed an intelligent method for the prediction of ticket prices based on the SSA-BP. This model not only makes full use of the powerful nonlinear modeling ability of the BP algorithm, but also takes advantage of the strong optimization ability and fast convergence speed of the SSA. Finally, this study screened out the key input factors by adopting the MIV method to simplify the structure of the BP algorithm and achieve a high prediction accuracy. In this study, Beijing Subway Line 4, Wuhan Metro Line 2, and Chengdu Metro Line 1 were selected as case study sites. The results showed that the linear correlations between influencing factors and ticket price for the PPP subway system service were weak, which indicated the need for using nonlinear analysis methods such as the BP algorithm. Compared with other prediction methods (the price adjustment method based on PPP contract, the traditional BP algorithm, the BP neural network improved by the genetic algorithm, the BP algorithm improved by the particle swarm optimization, and the support vector machine), the model proposed in this paper showed better prediction accuracy and calculation stability.
Highlights
Numerous subway systems are being built around the world to meet the needs of rapid urban development [1]
Introduction to the mean impact value (MIV) Method. e MIV method proposed by Dombi et al [42] is considered as one of the best indexes to evaluate the correlation of variables in neural networks. e basic idea of applying the MIV method to the model developed in this study is to take the variables with a significant impact on the price of the ticket for the private partnership (PPP) subway system as the input parameters of the prediction model and to eliminate the variables that have less impact
In order to further analyze the advancement of the model proposed in this paper, we compare various prediction methods, including price adjustment method based on PPP contract, multiple regression, BP, GABP, particle swarm optimization (PSO)-BP, and support vector machine (SVM)
Summary
Numerous subway systems are being built around the world to meet the needs of rapid urban development [1]. Under the PPP mode, the private sector cooperates with the government to participate in the construction and operation of subway systems. E private sector is mainly responsible for the financing, construction, operation, and maintenance of subway systems. Erefore, predicting the ticket price rapidly and Complexity accurately is of critical importance to ensure an acceptable return on the funds invested by the private sector and the welfare level of the public, which decide whether the subway system could be implemented smoothly. E training samples to train the SSA-BP neural network are selected, and the prediction model Ptrain is obtained. According to the MIV value, the extent of the influence of each characteristic variable on ticket price is obtained
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