A boarding model for heterogeneous passengers on the platform of high-speed railway station

Li, Chuan-Yao; Yang, Rui-Yu; Chen, Liang; Tang, Tie-Qiao

doi:10.1016/j.simpat.2020.102188

Cited by 18 publications

(3 citation statements)

References 35 publications

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“…Some have conducted simulation analyses in the laboratory (Lee et al, 2007;Seriani et al, 2017;Zhang et al, 2008), while others have conducted station observation surveys to explore a range of factors such as coach passenger distribution and passenger numbers difference that influence such results (Lee et al, 2007;Tang et al, 2017). Moreover, some trains such as the High-speed Railway have fixed seat numbers, and seats are controllable during peak hours, so only differences in the distribution of standing passengers need to be considered (Li et al, 2021;Tang et al, 2017). Some studies have shown that the distribution of passengers inside the train is also highly related to the station layout, i.e., where the train stops (Qi et al, 2018;Sohn, 2013).…”

Section: Literature Reviewmentioning

confidence: 99%

The Variation of Passenger Loading within the Train and over the Time

Li,

Fujiyama

2024

Preprint

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To utilize the existing railroad transportation to solve the congestion problem, more knowledge is needed about the variation of passengers in the coaches. This study investigates passenger variation in coaches based on data from trains arriving at London Bridge station on three selected routes, Hayes, Bexleyheath and Sidcup. Specifically, the variation in coach loading with time and other factors were analyzed. These factors were also analyzed by linear and polynomial regression. According to the results of the data processing, the passenger loading variation is more closely related to the relationship between the station and the loading mean difference of the loading of the adjacent stations, while the factor of the time to arrive London Bridge Station from the originating station, is not necessarily related to the variation of the coach loading. The knowledge gained examines the variation of coach passengers and will be useful in designing railway stations and planning their operation.

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Section: Literature Reviewmentioning

confidence: 99%

The Variation of Passenger Loading within the Train and over the Time

Li,

Fujiyama

2024

Preprint

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“…It is possible to find studies that identify problems and offer improvement suggestions by analyzing the boarding process. It is analyzed the boarding process by examining the movements of passengers and the confusion that could hinder the movement of passengers on the platforms and identified factors that reduce boarding efficiency [54]. It is conducted a study on improving the processes in boarding facilities for disabled passengers [55].…”

Section: Classic Boarding Processmentioning

confidence: 99%

Internet of Things Supported Airport Boarding System and Evaluation with Fuzzy

Memika¹,

Polat²

2023

Intelligent Automation &Amp; Soft Computing

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The existing systems sustained with the investments made require more automation and digital transformation with the continuous advancement of technology. The aviation industry is a sector that is open to more automation and digital transformation, mainly because of the intense competition and the analysis of a large variety of data. The long duration of operations in current airline processes and some process flows cause customer dissatisfaction and cost increase. In this study, the boarding process, which is one of the operational processes of airline transportation and is open to improvement, was discussed. The classical boarding process has been redesigned using Internet of Things technology a model called Boarding 4.0 was created. With Boarding 4.0, it is aimed to design a process where passengers can take their time before boarding more efficiently. In the study, the sub-processes of the Boarding 4.0 model, other processes that the sub-processes interact with, their activities, and data exchange passenger movements during the activities are explained in detail. Compared to the classical boarding process and Boarding 4.0 with the fuzzy ahp technique, it has been shown that boarding 4.0 is more advantageous and passenger movement times can be reduced during boarding. As a result of the evaluation made with the fuzzy ahp, it was determined that boarding 4.0 is more advantageous than the classical boarding process. In addition, when the total time of the sub-activities in the boarding process is calculated, boarding activities for a passenger take 50 min with the classic boarding process and 20 min with Boarding 4.0. Thus, when Boarding 4.0 is used, the passenger gains 30 min. Furthermore, when the calculation is made concerning the airport's current capacity, two passengers are hosted with the classical boarding process, while five passengers are hosted with Boarding 4.0. This acquisition is significant for airports in terms of efficient use of resources.

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“…In addition to pedestrian speed heterogeneity, there are other characteristics of pedestrian heterogeneity, such as baggage preference, proposed by Li et al [14]; prediction range heterogeneity, suggested by Suma et al; and left-right avoidance preference, put forward by Guo et al [15]. It also has a greater impact on the movement characteristics of pedestrians.…”

Section: Introductionmentioning

confidence: 99%

Subdivided Cellular Automata Model Considering Anticipation Floor Field and Analysis of Pedestrian Detour Behavior

Liu

Shu

2021

Sustainability

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The micro-pedestrian simulation model represented by the cellular automata model is an important simulation model. Improvements in various aspects enable a better description of the various behaviors of pedestrians, such as pedestrian avoidance behavior, companion behavior, as well as transcendence behavior, waiting behavior and detour behavior. This paper takes the pedestrian detour behavior in the circle antipode experiment as the main entry point. The subdivision cellular automaton model is integrated into the prediction field to model and simulate the detour behavior. At the same time, it explores the degree of subdivision of the cell. Pedestrian heterogeneity and the influence of predicted field potential energy on the simulated pedestrian trajectory. Finally, based on the temporal and spatial indicators of pedestrian trajectory characteristics, the KS test and DTW method are used to evaluate the similarity of the trajectory distribution characteristics and time series characteristics with experimental samples, and evaluate and compare models with or without heterogeneity. The results show that the trajectory characteristics of heterogeneous pedestrians are closer to the experiment than homogeneous pedestrians.

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A boarding model for heterogeneous passengers on the platform of high-speed railway station

Cited by 18 publications

References 35 publications

The Variation of Passenger Loading within the Train and over the Time

The Variation of Passenger Loading within the Train and over the Time

Internet of Things Supported Airport Boarding System and Evaluation with Fuzzy

Subdivided Cellular Automata Model Considering Anticipation Floor Field and Analysis of Pedestrian Detour Behavior

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