The modeling of attraction characteristics regarding passenger flow in urban rail transit network based on field theory

Abstract

Aimed at the complicated problems of attraction characteristics regarding passenger flow in urban rail transit network, the concept of the gravity field of passenger flow is proposed in this paper. We establish the computation methods of field strength and potential energy to reveal the potential attraction relationship among stations from the perspective of the collection and distribution of passenger flow and the topology of network. As for the computation methods of field strength, an optimum path concept is proposed to define betweenness centrality parameter. Regarding the computation of potential energy, Compound Simpson’s Rule Formula is applied to get a solution to the function. Taking No. 10 Beijing Subway as a practical example, an analysis of simulation and verification is conducted, and the results shows in the following ways. Firstly, the bigger field strength value between two stations is, the stronger passenger flow attraction is, and the greater probability of the formation of the largest passenger flow of section is. Secondly, there is the greatest passenger flow volume and circulation capacity between two zones of high potential energy.

Public Norms in the Operation Scheme of Urban Rail Transit Express Trains: The Case of the Beijing Changping Line

“…The characteristics of urban passenger flow form the basis of creating a train operation scheme. Li et al [2] studied the problem of passenger flow gravity in rail transit and proposed the concept of a passenger flow gravitational field. Zhao et al [3] studied the total passenger, section passenger and transit passenger flows and other aspects based on the passenger flow data from 2010 to 2016 and compared the results with those of Beijing, Shanghai and Guangzhou.…”

Understanding Daily Travel Patterns of Subway Users – An Example From the Beijing Subway

“…swiped out, the AFC system will fill in the previous record with the information of the exit time, line and station of the outbound [20]. Other information such as deal type, card type, ticket type, device ID, etc.…”

Prediction of Passenger Flow in Urban Rail Transit Based on Big Data Analysis and Deep Learning

“…Jiang et al [9] decomposed the intraday passenger flow data into sequences of multiple scales, observed the chaotic features of the data, and reconstructed the phase space. Li et al [10] used the Lyapunov exponent to prove the obvious chaotic features of passenger/traffic flow data, and then processed passenger flow data according to phase space reconstruction theory and fractal theory. Hou et al [11] estimated the short-term traffic flow by the k-nearest neighbors (k-NN) model and the linear time series model, and learned that the k-NN model has certain advantages over the other model.…”