Abstract
Thank to developed SD model, decision makers can create appropriate policy. The importance of passenger numbers in compartments increased due to COVID-19, and managers didn't want to increase passengers in compartments too much. In this study, a model will be developed in Vensim package program. The model will be developed for analyzing passenger flows. Different scenarios can be tested thanks to developed system dynamics model. Subway passenger flow was analyzed via system dynamics. The SD model was developed in Vensim PLE package program. Passenger flow was defined as rate, and stations are defined as stock. Managers would change timing according to time, and effects of these changes can be observed via the model.
TopA Short Literature Review
Zhong et.al.(2020), created a CPS-enhanced subway operations safety framework using the concept of CPS and short-term prediction techniques of subway passenger flow; our framework is characterized by a “flexible and controllable, real-time operation” composed of six components: system, adjust, facilities, early warning, time control, and yielding. In the framework, the forecasting methods of subway passenger flow are the core, and cyber-physical systems are used to couple other components into a safety management information platform in which the CPS is responsible for sensing, control, and feedback in the entire operating process.
Zhang et.al.(2020), proposed a deep learning architecture combining the residual network, graph convolutional network and long short-term memory to forecast short-term passenger flow in urban rail transit on a network scale. This study provides subway operators with insight into short-term passenger flow forecasting by leveraging deep learning models.
Li et.al.(2020), proposed a multi-sites prediction method (MSP) of passenger flow in subway station. Real time passenger flow data collected from multi-sites in a subway station is used as inputs, and delay parameter is introduced to identify the correlation between measurements at multiple sites in this paper. In order to achieve a stable predictive effect, wavelet decomposition and reconstruction are used to process the data.
Sun et.al. (2020), proposed under the condition of network operation, considering the correlation of passenger flow congestion between multiple stations on the metro network, and the lagging effect of measures controlling passenger flow at stations, a simulation-based network coordinated passenger inflow control method. The influence of stranded passengers on the implementation effect of passenger control scheme is considered leading to the proposal of a network coordinated passenger inflow control model and time-division optimization method based on the simulation.
Jia et.al.(2020), studied on this topic and proposes a passenger-oriented network capacity calculation method. They studied on the case study Beijing subway network to calculate and analyze network capacity in different level of service requirement during peak hours. It is found out that transfer stations are the bottlenecks that constrain network capacity.
Key Terms in this Chapter
Vensim PLE Package: It is a package program that can be analyzed to SD model.
Stocks: Stocks are the accumulations within the system.
Dynamic Complexity: This arises from connections and disconnections that link social and business systems.
System Dynamics (SD): System dynamics is basically a process modeling technique.
System Dynamic Language: This language consists of four components: flows, stocks, decision functions, and information flow.