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
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.
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