scholarly journals Analyzing Influencing Factors of Transfer Passenger Flow of Urban Rail Transit: A New Approach Based on Nested Logit Model Considering Transfer Choices

2021 ◽  
Vol 18 (16) ◽  
pp. 8462
Author(s):  
Zhenjun Zhu ◽  
Jun Zeng ◽  
Xiaolin Gong ◽  
Yudong He ◽  
Shucheng Qiu
Keyword(s):  
Influencing Factors ◽  
Urban Rail Transit ◽  
Node Degree ◽  
Nested Logit ◽  
Rail Transit ◽  
City Center ◽  
Passenger Flow ◽  
Urban Rail ◽  
Transfer Stations ◽  
The City

With the continuous improvement of the operation line network of urban rail transit, analyzing influencing factors of transfer passenger flow of urban rail transit is critical to improve the transfer demand analysis of urban rail transit. Using data collected from questionnaires, transfer passenger flow surveys and smart cards, this study proposes an approach base on nested logit passenger flow assignment model considering transfer choice behaviours of passengers. The transfer passenger flow at seven transfer stations in Nanjing is obtained. Subsequently, this study investigates the potential influencing factors of transfer passenger flow, including the node degree, geographic location (located in the city center, urban fringe, suburbs or suburban fringe), economic location (distance from the city center) and transportation locations (if it is close to a transportation hub or in combination with the hub) of rail transit transfer stations. The results indicate that a positive correlation between the transfer passenger flow and the node degrees of transfer stations. However, the relationship between transfer passenger flow and the economic, geographic, and transportation locations of transfer stations is not clear. The finding have reference value for the network design of rail transit transfer stations and transfer facilities, and provide reference for the analysis of passenger flow under network operation.

A cooperative evacuation strategy for mass passenger flow in urban rail transit transfer stations

2020 ◽  
pp. 2150007
Author(s):  
Jia Hong-Fei ◽  
Sang Heng ◽  
Luo Qing-Yu ◽  
Yang Jin-Ling ◽  
Miao Hong-Zhi
Keyword(s):  
Real Life ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Nsga Ii ◽  
Transit System ◽  
Passenger Flow ◽  
Evacuation Strategy ◽  
Urban Rail ◽  
The Status ◽  
Transfer Stations

In reviewing the evacuation problem of mass passenger flow in urban rail transit transfer stations, the cooperative evacuation strategy considering urban rail transit and emergency bus simultaneously is found to be an efficient way. In this work, firstly, the dynamic characteristics of mass passenger flow are analyzed based on the abstraction and simplification of major entities of urban rail transit, including passengers, stations and trains. Then, the operations of the urban rail transit system are modeled, including the boarding, landing and transferring processes of the passengers and the status updating flows of the trains and stations. To realize the cooperative evacuation, a multi-objective optimization model considering the evacuation speed, the number of passengers transferred, and the amount of emergency buses is proposed. The NSGA-II algorithm is adopted to solve the proposed model, which can balance the theoretical validity and computational convenience. Last, the proposed strategy is applied in a real-life case based on the Shanghai Metro line, and the results verify its effectiveness and efficiency.

Passenger Flow Forecast of Urban Rail Transit Based on Support Vector Regression

2013 ◽  
Vol 433-435 ◽  
pp. 612-616 ◽  
Author(s):  
Bin Xia ◽  
Fan Yu Kong ◽  
Song Yuan Xie
Keyword(s):  
Support Vector Regression ◽  
Risk Function ◽  
Urban Rail Transit ◽  
Support Vector ◽  
Rail Transit ◽  
Risk Minimization ◽  
Promising Alternative ◽  
Minimization Principle ◽  
Passenger Flow ◽  
Urban Rail

This study analyses and compares several forecast methods of urban rail transit passenger flow, and indicates the necessity of forecasting short-term passenger flow. Support vector regression is a promising method for the forecast of passenger flow because it uses a risk function consisting of the empirical error and a regularized term which is based on the structural risk minimization principle. In this paper, the prediction model of urban rail transit passenger flow is constructed. Through the comparison with BP neural networks forecast methods, the experimental results show that applying this method in URT passenger flow forecasting is feasible and it provides a promising alternative to passenger flow prediction.

Operation Organization at Urban Rail Transit Station under Large Passenger Flow

2012 ◽  
Vol 253-255 ◽  
pp. 1995-2000
Author(s):  
Qiao Mei Tang ◽  
Li Ping Shen ◽  
Xian Yong Tang
Keyword(s):  
Transport Process ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Common Condition ◽  
Urban Transit ◽  
Passenger Flow ◽  
Operation Process ◽  
Urban Rail ◽  
Transit Station ◽  
Transit Operation

large passenger flow is a common condition of urban transit operation, and the station bears the pressure of large passenger flow directly. This paper analyzes the reason for the appearance of large passenger flow and the characteristics of it, discusses the principles and methods that the station can apply under large passenger flow combined with the passenger’s transport process and the operation process.

Collaborative passenger flow control of urban rail transit network considering balanced distribution of passengers

2021 ◽  
pp. 2150461
Author(s):  
Xiang Li ◽  
Yan Bai ◽  
Kaixiong Su
Keyword(s):  
Flow Control ◽  
Programming Model ◽  
Flow Characteristics ◽  
Service Level ◽  
Urban Traffic ◽  
Urban Rail Transit ◽  
Control Measures ◽  
Rail Transit ◽  
Passenger Flow ◽  
Urban Rail

The increase of urban traffic demands has directly affected some large cities that are now dealing with more serious urban rail transit congestion. In order to ensure the travel efficiency of passengers and improve the service level of urban rail transit, we proposed a multi-line collaborative passenger flow control model for urban rail transit networks. The model constructed here is based on passenger flow characteristics and congestion propagation rules. Considering the passenger demand constraints, as well as section transport and station capacity constraints, a linear programming model is established with the aim of minimizing total delayed time of passengers and minimizing control intensities at each station. The network constructed by Line 2, Line 6 and Line 8 of the Beijing metro is the study case used in this research to analyze control stations, control durations and control intensities. The results show that the number of delayed passengers is significantly reduced and the average flow control ratio is relatively balanced at each station, which indicates that the model can effectively relieve congestion and provide quantitative references for urban rail transit operators to come up with new and more effective passenger flow control measures.

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