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We show that the many unusual features of China's financial markets are consistent with a government choosing regulations to maximize a standard type of social welfare function.Under certain conditions, these regulations are equivalent to imposing explicit taxes on business and interest income, yet should be much easier to enforce. The observed implicit tax rates are broadly in line with those observed in other countries. The theory also forecasts, however, that China will face increasing incentives over time to shift to explicit taxes.

Railways are greatly threatened by geological hazards whose disastrous effects include severe economic losses as well as serious casualties. It is vital to properly account for such geological hazardous impacts during a railway alignment optimization process. However, geological factors are complex, especially in mountainous regions. Besides, economic factors are also crucial in railway alignment design. Therefore, railway alignment optimization can be termed as a cost-hazard bi-objective decision-making process. So far, least-cost railway alignment optimization has been studied quite thoroughly while the complicated geological hazard factors have received relatively little attention. In this study, a bi-objective alignment optimization model considering cost and geological hazard is developed. A novel geological railway alignment optimization model is proposed, which includes spatial geological constraints and geological hazard evaluations, after geological railway alignment design criteria are presented and analyzed for three kinds of typical geological hazards: debris flows, landslides, and rockfalls. The geological hazard evaluation includes geological susceptibility and vulnerability assessments. Then, this model is integrated with a previous least-cost alignment optimization model to construct a cost-hazard bi-objective model. The alignment searching processes are also improved to solve the proposed model by integrating geological-constraints-handling and bi-objective alignment optimization approaches. Finally, the effectiveness of the proposed method is verified by applying it to a complicated real-world case. The results show that the proposed method can produce less expensive and safer solutions than the best alignment designed by experienced human designers while satisfying all required design standards. Moreover, the method's applicability for solving actual problems is further demonstrated through the sensitivity analysis. 1 INTRODUCTION Geological hazards greatly threaten the construction and operation of railways. Their disastrous effects include not

The urban rail transit line operating in the express and local train mode can solve the problem of disequilibrium passenger flow and space and meet the rapid arrival demand of long-distance passengers. In this paper, the Logit model is used to analyze the behavior of passengers choosing trains by considering the sensitivity of travel time and travel distance. Then, based on the composition of passenger travel time, an integer programming model for train stop scheme, aimed at minimizing the total passenger travel time, is proposed. Finally, combined with a certain regional rail line in Shenzhen, the plan is solved by genetic algorithm and evaluated through the time benefit, carrying capacity, and energy consumption efficiency. The simulation result shows that although the capacity is reduced by 6 trains, the optimized travel time per person is 10.34 min, and the energy consumption is saved by about 16%, which proves that the proposed model is efficient and feasible.

Severe short-pitch rail corrugation was found to have occurred on four types of track on the same metro line. Field investigations found that, even with the same operation conditions, the corrugations had different wavelengths for the different types of track. Impact hammer tap tests were conducted to investigate the dynamic behaviour of the tracks. The test results showed that, in the investigated metro, short-pitch corrugations are associated with the resonance behaviour of the tracks. The test results also showed that the corrugations on the investigated tracks are not caused by torsional vibration due to the wheelsets. Numerical simulations were conducted to identify the resonance behaviour that is could not be observed in the impact hammer tests due to the limitations in the test method. Three-dimensional finite element models for the four types of track were established and they were used to study the dynamic characteristics of the different tracks. The resonance frequencies and modes that are related to the generation of the corrugation were clearly identified in the numerical modelling studies; this further verifies the relationship between the formation of corrugation and the resonance behaviour of the tracks. The effect of a low value of the fastener stiffness on the dissipation of wheel/ rail vibration energy was investigated with the help of numerical simulations. Both experimental and numerical results showed that the resonance behaviour of track structures is of great importance in determining the initiation, characteristics and development of the short-pitch corrugation on the investigated tracks.

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The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

Short-term passenger flow prediction in urban rail transit plays an important role because it in-forms decision-making on operation scheduling. However, passenger flow prediction is affected by many factors. This study uses the seasonal autoregressive integrated moving average model (SARIMA) and support vector machines (SVM) to establish a traffic flow prediction model. The model is built using intelligent data provided by a large-scale urban traffic flow warning system, such as accurate passenger flow data, collected using the Internet of things and sensor networks. The model proposed in this paper can adapt to the complexity, nonlinearity, and periodicity of passenger flow in urban rail transit. Test results on a Beijing traffic dataset show that the SARI-MA–SVM model can improve accuracy and reduce errors in traffic prediction. The obtained pre-diction fits well with the measured data. Therefore, the SARIMA–SVM model can fully charac-terize traffic variations and is suitable for passenger flow prediction.