Statistical Analysis of Passenger Flow Characteristics at Shanghai Pudong International Airport

As high vessel traffic in fairways is likely to cause frequent marine accidents, understanding vessel traffic flow characteristics is necessary to prevent marine accidents in fairways. Therefore, this study conducted semi-continuous spatial statistical analysis tests (the normal distribution test, kurtosis test and skewness test) to understand vessel traffic flow characteristics. First, a vessel traffic survey was conducted in a designated area (Busan North Port) for seven days. The data were collected using an automatic identification system and subsequently converted using semi-continuous processing methods. Thereafter, the converted data were used to conduct three methods of spatial statistical analysis. The analysis results revealed the vessel traffic distribution and its characteristics, such as the degree of use and lateral positioning on the fairway based on the size of the vessel. In addition, the generalization of the results of this study along with that of further studies will aid in deriving the traffic characteristics of vessels on the fairway. Moreover, these characteristics will reduce maritime accidents on the fairway, in addition to establishing the foundation for research on autonomous ships.

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Evaluation of subway passengers transfer hub congestion based on queuing model and TOPSIS method

Journal of Computational Methods in Sciences and Engineering ◽

10.3233/jcm-215101 ◽

2021 ◽

pp. 1-22

Author(s):

Xiaokun Wang ◽

Dong Ni

Keyword(s):

Queuing Theory ◽

Evaluation Model ◽

Flow Characteristics ◽

Distribution Area ◽

Flow Density ◽

Topsis Method ◽

Queuing Model ◽

Service Facility ◽

Passenger Flow ◽

Topsis Technique

To scientifically and reasonably evaluate and pre-warn the congestion degree of subway transfer hub, and effectively know the risk of subway passengers before the congestion time coming. We analyzed the passenger flow characteristics of various service facilities in the hub. The congested area of the subway passenger flow interchange hub is divided into queuing area and distribution area. The queuing area congestion evaluation model selects M/M/C and M/G/C based on queuing theory. The queuing model and the congestion evaluation model of the distribution area select the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. Queue length and waiting time are selected as the evaluation indicators of congestion in the queuing area, and passenger flow, passenger flow density and walking speed are selected as the evaluation indicators of congestion in the distribution area. And then, K-means cluster analysis method is used to analyze the sample data, and based on the selected evaluation indicators and the evaluation model establishes the queuing model of the queuing area and the TOPSIS model of the collection and distribution area. The standard value of the congestion level of various service facilities and the congestion level value of each service facility obtained from the evaluation are used as input to comprehensively evaluate the overall congestion degree of the subway interchange hub. Finally we take the Xi’an Road subway interchange hub in Dalian as empirical research, the data needed for congestion evaluation was obtained through field observations and questionnaires, and the congestion degree of the queue area and the distribution area at different times of the workday was evaluated, and the congestion of each service facility was evaluated. The grade value is used as input, and the TOPSIS method is used to evaluate the degree of congestion in the subway interchange hub, which is consistent with the results of passenger congestion in the questionnaire, which verifies the feasibility of the evaluation model and method.

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Passenger Flow Characteristics and Bus Rapid Transit: A Case Study of BRT in Xiamen, China

CICTP 2012 ◽

10.1061/9780784412442.145 ◽

2012 ◽

Author(s):

Yueer Gao ◽

Xiaohong Chen ◽

Ming Ding ◽

Yiwei Ma

Keyword(s):

Flow Characteristics ◽

Bus Rapid Transit ◽

Rapid Transit ◽

Passenger Flow

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Likelihood‐free parameter estimation for dynamic queueing networks: Case study of passenger flow in an international airport terminal

Journal of the Royal Statistical Society Series C (Applied Statistics) ◽

10.1111/rssc.12487 ◽

2021 ◽

Author(s):

Anthony Ebert ◽

Ritabrata Dutta ◽

Kerrie Mengersen ◽

Antonietta Mira ◽

Fabrizio Ruggeri ◽

...

Keyword(s):

Parameter Estimation ◽

Queueing Networks ◽

Free Parameter ◽

International Airport ◽

Passenger Flow ◽

Airport Terminal

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

Modern Physics Letters B ◽

10.1142/s0217984921504613 ◽

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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