A game theoretical model for the stimulation of public cooperation in environmental collaborative governance

Digital technologies provide a convenient way for the public to participate in environmental governance. Therefore, by means of a two-stage evolutionary model, a new mechanism for promoting public cooperation is proposed to accomplish environmental collaborative governance. Interactive effects of government–enterprise environmental governance are firstly explored, which is the external atmosphere for public behaviour. Second, the evolutionary dynamics of public behaviour is analysed to reveal the internal mechanism of the emergence of public cooperation in environmental collaborative governance projects. Simulations reveal that the interaction of resource elements between government and enterprise is an important basis for environmental governance performance, and that governments can improve this as well as public cooperation by increasing the marginal governance propensity. Similarly, an increase in the government's fixed expenditure item of environmental governance can also significantly improve government–enterprise performance and public cooperation. And finally, the effect of government's marginal incentive propensity on public environmental governance is moderated by enterprises' marginal environmental governance propensity, so that simply increasing the government's marginal incentive propensity cannot improve the evolutionary stable state of public behaviour under the scenario where enterprises’ marginal environmental governance propensity is low

Valuation method of intellectual property pledge financing based on income interval analysis and risk adjustment coefficient

Abstract This study has developed a new method to valuate intellectual property for pledge financing. First, based on interval theory and the relevant calculation rules, the income interval model is and then used to calculate the interval values of intellectual property. Second, based on the change structure of risk indicators, the AHP and set-valued statistics are utilized to calculate the risk adjustment coefficient. Third, the point value of intellectual property is calculated with its values at an interval scale and risk adjustment coefficient. The values of intellectual property at an interval scale provide the two parties negotiating pledge financing with a reference range of loan amounts. The risk adjustment coefficient becomes a crucial indicator for measuring value evaluation risk. The point value of intellectual property specifies how much the bank loan amount can deviate from the values of intellectual property at an interval scale. The method creates a multi-indicator system to valuate intellectual property for pledge financing, which lowers the risk of intellectual property pledge financing to a significant extent and facilitates its operation. Moreover, the method has been proven to be efficient in practice

Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System.

Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed) collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN), two learning processes are proposed: (1) a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2) a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE), root mean square error (RMSE), and mean absolute relative error (MARE) are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR), instantaneous model (IM), linear model (LM), neural network (NN), and cumulative plots (CP)

A Data-Driven Timetable Optimization of Urban Bus Line Based on Multi-Objective Genetic Algorithm

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Simulation results for considering influence of entry and exit ramp.

<p>Simulation results for considering influence of entry and exit ramp.</p

The estimated travel time in three periods for link in I90.

<p>(a) Estimation comparison in five days. (b) Peak hours. (c) Non-peak hours.</p

Links in application with ramps.

<p>(a) Case 1. (b) Case 2. (c) Case 3.</p

Simulation links with on and off ramp.

<p>Simulation links with on and off ramp.</p

Segment information of links in simulation.

<p>Segment information of links in simulation.</p