A taxi stand can effectively regulate the behavior of taxi picking up passengers, reduce empty-run rate, and provide a convenient and orderly waiting environment for the public. However, the unreasonable setting of the existing taxi stands in most cities leads to an extremely low utilization rate and a waste of public space resources. This paper presents a novel three-stage strategy to address the taxi stands location problem (TSLP) incrementally. First, taxi demands hotspots are mined from a massive taxi Global Positioning System (GPS) data with GIS platform, and the optimal area for taxi stands siting in the following stages is determined. Then, the spatial interaction between taxi demands and taxi stands is explored to generate demand subsections and stand candidates along both the sides of the road. At last, a taxi stand location model (TSLM) is developed to minimize the total cost, which contains the access cost of passengers and the construction cost of taxi stands. The genetic algorithm-based procedure is adopted for TSLM optimization. A case study conducted in China verifies the effectiveness of the location strategy and investigate the impact of the maximum acceptable distance for passengers on TSLP. The experimental results describe the number and layout of taxi stand under a different demand coverage, which indicates that the proposed approach is beneficial to provide scientific reference for the municipal department in taxi stand site decisions and make a tradeoff between the interests of planners and users.INDEX TERMS Taxi stand, location strategy, spatial-temporal demand, GPS big data, genetic algorithm.

In tokamak discharge experiments, the plasma position prediction model’s research is to understand the law of plasma motion and verify the correctness of the plasma position controller design. Although Maxwell equations can completely describe plasma movement, obtaining an accurate physical model for predicting plasma behavior is still challenging. This paper describes a deep neural network model that can accurately predict the HL-2A plasma position. That is a hybrid neural network model based on a long short-term memory network. We introduce the topology, training parameter setting, and prediction result analysis of this model in detail. The test results show that a trained deep neural network model has high prediction accuracy for plasma vertical and horizontal displacements.

The modeling and control of the plasma equilibrium response is still one of the more important research areas in tokamak discharge experiments. Although theoretically, first principles can predict the plasma instability, how to build a physical model for accurate prediction is still a challenging problem. Therefore, a deep learning method is proposed to model the plasma vertical displacement system in the HL-2A tokamak experiment, whose method expands the modeling strategy for tokamak plasma control systems. Through the training of a large number of high-dimensional experimental data, the obtained deep neural network model in this paper has a higher precision prediction ability. Additionally, to illustrate the significance of the predictive model in controller design, a data-driven adaptive control algorithm is proposed to replace the traditional proportional-integral-derivative control algorithm for controlling the vertical displacement of plasma. The simulation results showed that the proposed algorithm had less adjustable parameters, strong self-adaptability, and effective control for the HL-2A plasma vertical displacement.

To estimate the capacity of roundabouts more accurately, the priority rank of each stream is determined through the classification technique given in the Highway Capacity Manual 2010 (HCM2010), which is based on macroscopical analysis of the relationship between entry flow and circulating flow. Then a conflict matrix is established using the additive conflict flow method and by considering the impacts of traffic characteristics and limited priority with high volume. Correspondingly, the conflict relationships of streams are built using probability theory. Furthermore, the entry capacity model of roundabouts is built, and sensitivity analysis is conducted on the model parameters. Finally, the entrance delay model is derived using queuing theory, and the proposed capacity model is compared with the model proposed by Wu and that in the HCM2010. The results show that the capacity calculated by the proposed model is lower than the others for an A-type roundabout, while it is basically consistent with the estimated values from HCM2010 for a B-type roundabout.

Unbalanced directional traffic, which often exists at intersection approaches, is an important factor to induce traffic congestion on urban streets. Considering the settings of variable approach lanes, the presence of left-turn bays, and a variety of vehicle categories on an arterial road, an optimization model that minimizes the total delay is formulated and a control method that coordinates a variable sign and the corresponding signal group is put forward. To design the signal control scenarios with time of day, the procedure for using the proposed methodology is also presented in practice. To verify the given methodology, a case study is implemented using the field data and the four scenarios. The results reveal that the new methodology can better respond to the time-varying traffic flow at intersection approaches, and the provision of variable approach lanes and left-turn bays are helpful to reduce the average delay and enhance the average speed.