In this paper, the electric field resonance (EFR) method, similar to the four-coil configuration of the magnetic field resonance wireless power transfer, is proposed for the capacitive coupling power transfer. The characteristics of the proposed method are derived and analyzed. With the EFR method, not only unity power factor for the power source is achieved, also high power factor and low reactive power for the capacitive coupling stage are achieved. Effective power transfer is realized by the EFR method. Based on the proposed method, a dynamic charging concept for railway vehicles is then proposed. A prototype powering system is designed and built to prove the validity of the proposed method. Analytical, simulation and experimental results are given and compared. A 23 cm model vehicle is put on a 150 cm track. It is shown that about 700W power is transferred through a 24 pF coupling capacitor. The proposed method reaches 91% DC-DC overall efficiency at switching frequency 2 MHz.Index Terms-Capacitive power transfer (CPT), electric fielding resonance, dynamic charging. interests include simulation of wireless power transfer and electric-field coupled power transfer.Chunyan Shuai received the B.S. and M.S. degrees in control science engineering and computer application science from Kunming

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Examining how travel distance is associated with travel mode choice is essential for understanding traveler travel patterns and the potential mechanisms of behavioral changes. Although existing studies have explored the effect of travel distance on travel mode choice, most overlook their non-linear relationship and the heterogeneity between groups. In this study, the correlation between travel distance and travel mode choice is explored by applying the random forest model based on resident travel survey data in Guiyang, China. The results show that travel distance is far more important than other determinants for understanding the mechanism of travel mode choice. Travel distance contributes to 42.28% of explanation power for predicting travel mode choice and even 63.24% for walking. Significant nonlinear associations and threshold effects are found between travel distance and travel mode choice, and such nonlinear associations vary significantly across different socioeconomic groups. Policymakers are recommended to understand the group heterogeneity of travel mode choice behavior and to make targeted interventions for different groups with different travel distances. These results can provide beneficial guidance for optimizing the spatial layout of transportation infrastructure and improving the operational efficiency of low-carbon transportation systems.

Real-time expressway traffic flow prediction is always an important research field of intelligent transportation, which is conducive to inducing and managing traffic flow in case of congestion. According to the characteristics of the traffic flow, this paper proposes a hybrid model, SSA-LSTM-SVR, to improve forecasting accuracy of the short-term traffic flow. Singular Spectrum Analysis (SSA) decomposes the traffic flow into one principle component and three random components, and then in terms of different characteristics of these components, Long Short-Term Memory (LSTM) and Support Vector Regression (SVR) are applied to make prediction of different components, respectively. By fusing respective forecast results, SSA-LSTM-SVR obtains the final short-term predictive value. Experiments on the traffic flows of Guizhou expressway in January 2016 show that the proposed SSA-LSTM-SVR model has lower predictive errors and a higher accuracy and fitting goodness than other baselines. This illustrates that a hybrid model for traffic flow prediction based on components decomposition is more effective than a single model, since it can capture the main regularity and random variations of traffic flow.

With the further liberalization of the electricity market of China, customers' requirements, characteristics, and distribution, as well as the quality, security, and reliability of power supplies without interruption, have received considerable attention from power companies, policymakers, and researchers. How to deeply explore the distribution characteristics of electricity customers and analyze their sensitivities to electricity blackouts has become an especially important problem. This paper takes over 0.1 billion data, collected by various smart devices of the Internet of Things in the power system of China, such as smart meters, intelligent power consumption interactive terminals, data concentrators, and other cross-platform data, for example, 95 598 telephone records, complaint information, user bills, user information, and maintenance records, as study objects, to analyze the consumption characteristics of power users. It has been found that there is a wide range of power users who pay different electricity bills; a long-tail distribution following a power law lies in the number of users versus their paid electricity bills. Meanwhile, there are two Pareto effects (2-8 rule): the number of residents and non-residents versus their electricity bills, and the number of large industrial users and general industry (business users) versus in their electricity consumption and bills. Then, a decision tree algorithm is proposed to capture the characteristics of electricity consumers and to recognize the crowd who is power blackout sensitive. The evaluation indexes and parameters of the decision tree are discussed in detail, and a comparison with other intelligent algorithms shows that the decision tree has a good recognition performance over that of others, and the characteristics used to identify the blackoutsensitive crowd is various. All the results state that except for economic factors, positive social effects should also be considered. Various marketing strategies to satisfy different requirements of power users should be provided to promote long-term relationships between the power companies and power customers. INDEX TERMS Blackout sensitivity, big data, decision tree, electricity market, Internet of Things, longtailed, Pareto effect. NOMENCLATURE Notations Description Y The dependent variable, or target variable. It can be ordinal categorical, nominal categorical or continuous. If Y is categorical with J classes, its class takes values in C = {1,. .. , }.