As the hub and carrier to transfer the passengers, the railway station is an important factor that affects the rail passenger transportation because the normal operation of the station without load redundancy is determined by the moderate passenger flow. It means reasonable and accurate prediction of passengers entering and leaving the station can provide the basis and guarantee for the station security, the resources allocation and the personnel deployment. Since the neural network model is good at processing the common regular data changes through training the network and adjusting the weight value based on a large number of training samples, the neural network model is used in processing the short-term irregular data to predict the passenger flow at the railway station which is susceptible to the constantly changing external factors. In this paper, the neural network is used to predict the passenger flow. First, the key factors affecting the change of the passenger flow are selected and analyzed as the input of the neural network. Second, the learning and the rate updating of variable step size are adopted to estimate the number people entering the station during a certain time interval, which is then weighted with the historical data to derive the prediction of the passenger flow during the next time interval. The simulation results show that the experiment results show that the method proposed in this paper can better track and predict the sudden changes in the passenger flow caused by emergencies. Meanwhile, it can be found that in the process of forecasting abnormal passenger flow, the most critical link is to summarize and summarize the characteristics of railway station passenger flow, clarify the type and time distribution of passenger flow at each station, and analyze the factors that cause abnormal changes in passenger flow.

No abstract

In this paper, we propose an edge-enhanced maximally stable extremal region (E-MSER) method in the multi-spectral image registration. To increase the detection rate of MSERs, an edgeenhanced image with an adjustment factor is well prepared in advance. Then, E-MSERs are detected based on the new one. Although the grey level of multi-spectral images varies a lot from different imaging bands, E-MSERs show a good stability. Scale-invariant feature transform descriptor can be used to describe the E-MSERs. Four criteria such as matching score, repeatability, precision and recall are applied to evaluate the detectors' performance and root mean square error is used to analyse the registration accuracy. The experiments made in multispectral images with same scene have shown that the E-MSER method performs better than the untouched MSER method. Moreover, comparative experiments have been made with E-MSER, MSER and some other feature detectors (e.g. Harris-Affine, Hessian-Affine and DoG-based) under the scenes of affine transformation. The values of evaluation criteria show that the E-MSER performs better than MSER. At the same time, the registration accuracies of E-MSER and MSER are ,1 pixel, which are much smaller than those of other detectors.

No abstract

The subway station has a large passenger flow and strong mobility, and subway-oriented signs have become the necessary core support for the subway system. The paper first takes the complaint data of Beijing Metro as the research object and uses Word2vec methods to effectively prove the phenomenon of subway-oriented identification. Next, it builds a MIP model for the subway-oriented identification system based on the optimization theory and analyzes the model. As an example, Dongzhimen Station of the subway has optimized the spatial layout of its guide signs. Dongzhimen station is an important transportation hub, there are some sign problem, such as long transfer distance, narrow transfer space, complicated traffic conditions, and obvious passenger flow aggregation. The amount of induced information in the area is the objective function, and an optimization model for the optimization of signs is established to help scientifically find the optimal placement of guide signs from multiple candidate positions. The model can theoretically reduce the guidance errors caused by the guidance marks, and will be verified by real cases in practical applications, thereby verifying the effectiveness of the model. The research results can provide reference and reference for subway passenger flow guidance.