No abstract

The transfer-operate-transfer (TOT) project model is used widely as a commercial framework for public-private-partnerships to support provision of infrastructure and enable the delivery of services. However, operational delivery of such projects can encounter certain challenges, such as the need for improved revenue sharing between governmental and private partners. The purpose of this paper is to design a revenue sharing method (RSM) that satisfies the revenue-sharing forecast in the contract design stage and the realized revenue sharing in the contract execution period for an operational TOT project. This approach identifies the impact of external uncertainty and effort level as well as the input ratio on revenue sharing of participants, distributes and reasonably minimizes the project revenue uncertainty among the participants, and achieves an improved matching of the participants’ revenue sharing with their risk-sharing, resource input and effort level. The paper utilizes the fuzzy-payoffs Shapley value method for revenue distribution for an operational TOT project, where the fuzzy alliance and input ratio coefficient are adopted to gradually optimize the Shapley value and form the RSM of an operational TOT project. The RSM allows prediction of the revenue sharing of participations under uncertain conditions of project revenue and supports improved decision-making by participants.

In view of the nonlinearity and uncertainty of safety accident risk assessment, firstly, based on the deep neural network, the training criterion of the network is changed, and the triplet convolutional neural network with the similarity measure as the cost function is proposed. The inactive multi-scale set features are extracted from them, so that the semantic features obtained by learning are suitable for security risk image retrieval. In the image retrieval application, the training samples of the retrieved data set are not enough to train a large network, and the innovative application of migration learning to security risk image retrieval proposes to train the network with data sets similar to the retrieved data sets. Then based on the traditional nearest neighbor algorithm, this paper proposes a case similarity calculation method based on two-dimensional structure of structural similarity and attribute similarity, input the characteristic attribute value of the current emergency event, and conduct similar case retrieval. The final calculation returns the historical case and its solution that the user is most similar to the currently entered incident feature. The experiment proves that the maximum relative error between the output of the network and the expected output value is 5.17%, and the minimum relative error is 1.38%, which has high accuracy.

In recent years, China’s government has encouraged the adoption of the TOT (Transfer-Operate-Transfer) model to realize the marketization of China’s public service stock projects. The TOT model is a cooperation mechanism through sharing investment, revenue and risks between the government and private partner. Therefore, a fair and reasonable revenue sharing method (RSM) is the key to the success of the TOT project. This paper aims to provide a fair and reasonable RSM based on a modified Shapley value with a triangular symmetric fuzzy structure element, which has better motivation, flexibility, forecasting function and dynamic precise distribution function. According to the factors that affect revenue sharing, the Shapley value is improved with initial correction coefficient composed of investment ratio, risk-sharing ratio, execution degree, and fuzzy payment to achieve fairness and reasonableness. The methodology is illustrated by a case study of a TOT project selected from Laohekou city of Hubei province, China. The results testify that the revenue-sharing ratios of participants is positively correlated with the initial correction coefficient, which make the RSM more motivating; and the Shapley value with fuzzy payment by using triangular symmetric fuzzy element function make the RSM more flexible, and it has both forecasting function and precise dynamic distribution function under project revenue uncertainty.