Two-Stage OD Flow Prediction for Emergency in Urban Rail Transit

Urban rail transit (URT) is vulnerable to natural disasters and social emergencies including fire, storm and epidemic (such as COVID-19), and real-time origin-destination (OD) flow prediction provides URT operators with important information to ensure the safety of URT system. However, hindered by the high dimensionality of OD flow and the lack of supportive information reflecting the real-time passenger flow changes, study in this area is at the beginning stage. A novel model consisting of two stages is proposed for OD flow prediction. The first stage predicts the inflows of all stations by Long Short-Term Memory (LSTM) in real time, where the dimension is reduced compared with predicting OD flows directly. In the second stage, the notion of separation rate, namely, the proportion of inbound passengers bounding for another station, is estimated. Finally, The OD flow is predicted by multiplying the inflow and separation rate. Experiments based on Hangzhou Metro dataset show the proposed model outperforms the contrast model in weighted mean average error (WMAE) and weighted mean square error (WMSE). Results also suggest that the proposed prediction model performs better on weekdays than on weekends, and with greater accuracy on larger OD flows. IEEE

CEEMDAN-IPSO-LSTM: A Novel Model for Short-Term Passenger Flow Prediction in Urban Rail Transit Systems.

Urban rail transit (URT) is a key mode of public transport, which serves for greatest user demand. Short-term passenger flow prediction aims to improve management validity and avoid extravagance of public transport resources. In order to anticipate passenger flow for URT, managing nonlinearity, correlation, and periodicity of data series in a single model is difficult. This paper offers a short-term passenger flow prediction combination model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and long-short term memory neural network (LSTM) in order to more accurately anticipate the short-period passenger flow of URT. In the meantime, the hyperparameters of LSTM were calculated using the improved particle swarm optimization (IPSO). First, CEEMDAN-IPSO-LSTM model performed the CEEMDAN decomposition of passenger flow data and obtained uncoupled intrinsic mode functions and a residual sequence after removing noisy data. Second, we built a CEEMDAN-IPSO-LSTM passenger flow prediction model for each decomposed component and extracted prediction values. Third, the experimental results showed that compared with the single LSTM model, CEEMDAN-IPSO-LSTM model reduced by 40 persons/35 persons, 44 persons/35 persons, 37 persons/31 persons, and 46.89%/35.1% in SD, RMSE, MAE, and MAPE, and increase by 2.32%/3.63% and 2.19%/1.67% in R and R2, respectively. This model can reduce the risks of public health security due to excessive crowding of passengers (especially in the period of COVID-19), as well as reduce the negative impact on the environment through the optimization of traffic flows, and develop low-carbon transportation.

A Study on the Intelligent Optimization of Subway Security Check Space in the Context of Epidemic

As one of the main means of transportation for citizens in Wuhan, urban rail transit has assumed the dual responsibility of ensuring the travel needs of citizens and blocking the spread of the epidemic in the context of COVID-19. Taking the security check space of Wuhan subway Street entrance station as an example, the paper aims at putting forward the optimization strategy of security space design to solve the obstruction problem caused by the excessive flow of subway stations at present. The paper takes the COVID-19 prevention and control requirements in Wuhan into consideration, uses intelligent technology, combines the construction of social force model to conduct pedestrian simulation, and applies simulation variable analysis. The findings indicate that the optimization strategy of security space design effectively shortens the arrival time and effectively controls the flow of people. It is expected to provide some reference and research basis for the design and optimization of security inspection space of subway transportation system in the future. © 2022 SPIE.

The impact of COVID-19 on subway passenger flow in Chicago: A study of spatial variation of influencing factors

At the beginning of 2020, with the rapid spread of COVID-19 around the world, the passenger flow of subway has suffered from a serious impact. Based on the subway passenger flow data in Chicago, this article analyzes the impact of COVID-19 on rail transit passenger flow. ArcGIS is used to visualize the spatial-temporal distribution of the passenger flow of different stations during different time periods. Based on the fluctuation characteristics of passenger flow before and after the outbreak of COVID-19, one of the deep learning methods, the LSTM (Long-Short Term Memory) neural network model, is constructed to predict the passenger flow of each station in the scenario of no virus. The decline of passenger flow is calculated for each station. Stepwise regression model is constructed to determine factors that explain the decline in passenger flow, and significant factors are obtained: the original passenger flow, number of houses and jobs within 800m buffer zone, number of bus stops within 800m buffer zone, whether the station is a transfer station, distance from the station to the city center, and the number of low-income people. The results of the study show that after the outbreak of COVID-19, the passenger flow of the subway in Chicago experience a 'cliff-like' decline in the short term. The passenger flow in most areas dropped by more than 80%, and the passenger flow of some severely impacted stations dropped by more than 90%. Characteristics of the station and built environment factors of different stations influence the decline of passenger flow. © 2021 IEEE.

BIM-based optimization of passenger flow organization in urban rail transit stations during the epidemic prevention and control phase

As a high-density crowd collection and dispersal carrier,rail transit is characterized by airtight confinement, which will provide an suitable environment for the potential spread of coronavirus disease. The virus distribution inside stations needs to be analyzed in order to adopt effective passenger flow organization strategies to reduce the risk of virus infection inside stations. With the help of aerodynamic principle, according to the virus transmission theory, the BIM simulation technology has been used to simulate the air flow direction of a certain station of Wuhan metro using Fluent software to qualitatively analyze the station's virus-prone gathering area and provide reference for station daily disinfection.The daily passenger flow organization of the station has been simulated considering the social force model.Comparing the overlap between the concentrated area of passenger flow density and the virus-prone area,a targeted imrovement plan of passenger flow organization has been proposed. The improvement measures have been verified quantitatively by two evaluation indexes: the average dwell time and virus susceptibility in pedestrian stations. The results show the effectiveness in reducing the probability of virus infection of passengers traveling in the station by disabling the station vending machines, reducing the number of passengers in the station, increasing the escalator rate, and optimizing the flow lines of passengers entering and exiting the station. The simulation results can provide inspiration and reference for normalized epidemic prevention in the daily operation of rail transit. © 2021 IEEE

Research and Calculation of Subway Safe Transport Capacity under Major Infectious Disease-caused Epidemic: A Case Study of Shenzhen Subway

The emergence of novel coronavirus-infected pneumonia has put tremendous pressure on the operation and organization of urban rail transit. This paper analyzes the calculation method of urban rail transit transport capacity and calculates the safe transport capacity of Shenzhen Metro under the major infectious disease-caused epidemic. This paper then uses Mass Motion passenger flow simulation software to simulate passenger flow to simulate the operation of the station and verify the carrying capacity of Shenzhen Metro stations under the conditions of epidemic prevention. Finally, targeted measures for managing passenger flow of subway under major infectious disease epidemics are proposed to provide ideas and experience for improving the level of subway safety operation and management under major infectious disease epidemics. © 2022 SPIE. All rights reserved.