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Health assessment for point machines is crucial to the safety of rail systems. The operation of the point machine is a typical multi‐stage process, each with its own characteristic features that allow a health assessment. Therefore, to segment various phases self‐adaptively is quite essential to assess the health state of the point machine. Besides, the degradation of the point machine is characterized as non‐linear. However, these issues are barely discussed when assessing the degradation degree. By converting it into a multi‐classification problem, this paper proposes a novel phase segmentation method based on dimensionless time‐domain features and characteristics of time series by utilizing the adaptive Multiclass Mahalanobis Taguchi System (aMMTS) to segment the signal self‐adaptively. Furthermore, this paper proposes a novel algorithm named Non‐linear Dynamic Time Warping (NLDTW), which modifies the conventional Dynamic Time Warping (DTW) by using a non‐linear distance to overcome the lack of global consistency in the non‐linear degradation assessment. Finally, a modified formula of confidence value is presented to assess the actual degradation degree. The efficiency and feasibility of the proposed framework have been verified by the actual data collected from the point machines of Guangzhou Metro. [ABSTRACT FROM AUTHOR]

In public places, even if pedestrians do not have their mobile devices connected with any WiFi access point (AP), WiFi probe requests will be broadcast, so that WiFi sniffers can be employed to crowdsource these WiFi probe packets for use. This paper tackles the problem of exploiting the passive WiFi sensing approach for pedestrian flow analysis. To be specific, a passive WiFi sensing model is first established based on a probabilistic analysis of interactions between WiFi sniffers and the moving pedestrian flow, capturing the main factors affecting pedestrian flow characteristics. On that basis, a sequential filtering algorithm is proposed based on the Rao-Blackwellized particle filter (RBPF) to produce simultaneous and efficient estimates of the pedestrian flow speed and pedestrian number utilizing the real-time sniffing results. In order to validate this study, an experimental pedestrian surveillance system using WiFi sniffers is deployed at the transfer channel of a metro station in Guangzhou, China. Extensive experiments are conducted to verify the passive sensing model, and confirm the effectiveness and advantages of the proposed algorithm. The pedestrian flow estimation not only helps to improve the safety and facility management and customer services, but also paves the way for introducing other novel applications. [ABSTRACT FROM AUTHOR]

Prediction of passenger flow distribution in urban rail transit stations can provide important data support for passenger flow organization and passenger travel guidance. However, complex station space structure and simulation-based passenger flow data bring challenges to accurate analysis and prediction of the passenger flow inside the station. This paper proposes a temporal graph attention convolutional neural network model (TGACN) to predict the passenger flow volume and density in key areas of the station. Firstly, considering the topological structure of key areas and the characteristics of passenger flow and flow trend in the station, a multi-graph generation method for continuous space in stations is designed, including geographic neighborhood graph and semantic neighborhood graph, to represent the static and dynamic correlation between nodes. Secondly, a new method of spatio-temporal feature fusion is proposed, which takes multi-graph as input to optimize the extraction and expression of spatial and temporal correlation. Finally, the TGACN is verified by passenger flow data set, which is constructed based on real-time video monitoring data of a transit station in Guangzhou. Experiments demonstrate that the TGACN can obtain the spatio-temporal correlation from passenger flow data, and the prediction results are better than the existing baseline models. • A prediction model for multipoint passenger flow in station is proposed. • Geographical graph is constructed to measure the spatial correlation between nodes. • Semantic graph is constructed to measure the correlation of passenger flow state. • Effective spatial information screening is realized by dimensionality reduction. [ABSTRACT FROM AUTHOR]

This paper is aimed at crowding phenomenon in the subway. As passengers are inclined to choose the route with minimum disutility, we put forward a route choice model which is constructed to achieve minimum objective function of feasibility for the optimal solution. Meanwhile we set passenger volume threshold values according to capacity of facilities. In the case of actual capacity exceeding the threshold, the decision node of constrained route will be selected; computing procedure about searching decision points will be presented. Then we should set rational restrictions at the decision node of the minimum utility function route to prevent too many passengers’ access to platform. Through certification, this series of methods can effectively ensure the safety of the station efficient operation. [ABSTRACT FROM AUTHOR]

• The improved brute-force search transforms the energy-saving operation problem into optimal turning point solution. • Considering the speed limit, a more reasonable energy-saving turning point is obtained by adding a constant speed section. • The energy-saving effect of improved brute-force search is better than GA and PSO algorithms in Guangzhou Metro Line 7. Rail train operation energy consumption mainly focuses on train traction energy consumption. Reducing train traction energy consumption in rail transit operation is significant to developing a green and low-carbon economy and reducing operation costs. The rail train operation energy-saving optimization framework is developed considering the utilization of regenerative braking energy. Firstly, three objectives of punctual arrival, fixed-point parking and minimum energy consumption are provided by train operation strategy analysis. Secondly, the improved brute-force search is developed to solve train operation energy-saving multi-objective problems. The running time, speed, distance, power, and energy consumption of operation intervals are calculated. Finally, Guangzhou Metro Line 7 is taken as an example to verify the effectiveness of the developed optimization model. The results show that the improved brute-force search method effectively finds a more energy-saving turning point under constant interval operation time and has a better energy-saving effect than two other heuristic algorithms. [ABSTRACT FROM AUTHOR]