Monday, June 1, 2020

Simulation of Ticket Hall Queuing behavior in Transit Station Based on Cellular Automate Model

Exam Hall Ticket Management System

Simulation of Ticket Hall Queuing behavior in Transit Station Based on Cellular Automate Model

MODELING APPROACH

Exam Hall Ticket Management System
A.Overview The approach models each pedestrian individually as an “agent”. The distribution of arrival follows the Poisson distribution. The movement of pedestrian is driven by tasks, for example, buying ticket and entering the toll area through ticket check gate. In the rush hour, the queuing behavior is the common phenomena. So, in the microscopic level, several behaviors should be modeled: pedestrian generation, search for goal (ticket window and ticket check gate), queuing, movement in the queue and the non queue area, and waiting for service etc. B.Modular level Any microscopic simulation system not only considers agent’s movement or action, but also modules that compute higher level strategies of the agents. In fact, it makes sense to consider the physical and the mental world completely separately (Fig. 2).

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The most important modules of the decision layer are: zActivity Generator. Being able to compute routes, as the route generator does, only makes sense if one knows the destinations for the agents. A new technique in transportation research is to generate a (say) day-long chain of activities for each agent, and each activity’s specific location. In the Ticket Hall area, activities include: queuing for tickets at the ticket windows (without ticket), and directly queuing for passing through ticket check gate (with ticket or after buying tickets). zRoute Generator. It is not enough to have agents walk around randomly; for realistic applications it is necessary to generate plausible routes. In terms of graph language, this means that agents need to compute the sequence of links that they are taking through the network. A typical way to obtain such paths is to use a Dijkstra best path algorithm. This algorithm values individual links based on generalized costs, such as distance and travel time. zLocomotion modular: after a series of decision process, agents get their new move direction and position at each next time step. In pedestrian behavior models, the locomotion is achieved by CA.
The paper mainly study the queuing behaviour in normal situation based on CA. from theory and experimentation, some conclusions is drawn: (1) From animation, CA is suitable to simulating pedestrian behaviours in normal situation. (2) The transition possibility function based on modified included angle reflects the route preference of pedestrian. Code Shoppy


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Tuesday, March 10, 2020

Design of an IoT-BIM-GIS

Designof an IoT-BIM-GIS


There has been a significant increase in emergency incidents involving hospital basic operation (non-clinical side of hospital daily running and maintaining), which adversely affects the functioning of hospitals and poses threat to the staff members and patients. To cope with these emergencies, our group proposes a design of risk management system based on technologies of Internet of Things (IoT), building information model (BIM), and geographic information system (GIS), aiming to realize real-time risk factors identification and more effective and more efficient coordinated response. In this paper, the system architecture, key technologies, and simulated cases are also presented respectively.Keywords-risk management, hospital basic operation, BIM, IoT, GIS 

]. The concept and techniques were later utilized by healthcare orgnisations, for administrating their daily clinical work, amid the growing tendency of dependence on advanced equipment and information system during the past century [2, 3]. In the meantime, risks in the non-clinical daily running and maintaining of hospitals, including logistics and ward, has received less attention from administrators and researchers. Through a survey of cases, we found that emergency incidents involving non-clinical basic operation could cause severe consequences threatening the health and safety of patients and staff members. For instance, the fever clinical building of the People’s Hospital of Shanxi province in China collapsed for land subsidence caused by the chronical leakage of underground water supple pipe networks, and the gas explosion in Cuajimalpa Maternal Hospital, located in southeast of Mexico City, caused 3 deaths and dozens of injuries [4, 5]. Such adverse events are typically caused by oversight, errors, and defects of the hospital basic operations and similar incidents happened pervasively. Risks of basic operation in hospitals involve many parts of human work and equipment running. The existing management strategy was mainly dependent on the manual inspection and disposal experience of staff members, which apparently increase the uncertainty of timely risk discovering and appropriate disposal measures taking. Informationized risk management system, based on IoT, GIS and BIM, contributes to solving these problems. IoT is defined as the internetworking of physical devices, buildings, vehicles and other objects, embedded with sensors, electronics and network connectivity that enable them to collect and transfer data [6]. In the risk management system for hospitals, IoT refers to the sensors collect and exchange the data about equipment running and environment and monitors providing the information
of the situation and state of crowded or other attention-required area. BIM is a process related to the generation and management of digital representations of physical and functional characteristics of places [7], which helps to locate the risks and incidents, supply the information (such as the combustible material, or pipe network nearby and etc.) of specific area, and present the neighboring scene in and out buildings. GIS is designed to capture, store, manipulate, analyze, manage, and present spatial or geographical data [8]. Not only display the data of longitude, latitude, and elevation, GIS is also characterized with the ability to realize related states analysis and prediction, paths planning and etc., that makes the procedure of emergencies coordinate response more orthonormal and considerate. The design of risk management system proposed in the paper integrates the technology of IoT, BIM and GIS to timely discover the risk and handle the emergencies in hospital basic operation.https://codeshoppy.com/shop/product/
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KEY TECHNOLOGIES
The realization of the system function requires the advancement in some fields: 1) Risk factors classification and multi-source heterogeneous data collecting and accessing: section 2.1 and 2.3 provide the solution and description; 2) Precise and accurate positioning and visualization of full risk factor: the function is much dependent on the deep integration of GIS, BIM and IoT. The detecting data and location of sensors provide the inputs for calculating and simulating tools of 3-dimentional GIS, while BIM presents the spatial settings and completes some constraint conditions. Utilizing the algorithm and tools embedded in the assessment model, the function of full risk factor visualization also covers correlation analysis, key performance indexes (KIP) analysis and so on. 3) Comprehensive situation awareness and joint response: the process takes account not only the risks sources themselves, but also the situation of neighboring space, which contributes to more considerate disposal procedures and resources mobilization, and detailedly illustrated in the two cases in section 4
APPLICATION CASES
The two use cases are the potential or expected use of the risk management system, which are preconceived ideas or now simulated in other software, such as ArcGIS, whose related function will be integrated into the system.

Pipeline
This case is based on ArcGIS, part involved function of which will be incorporate into risk management system. In this simulated scene, the pressure sensors detect the abnormality in pipeline parameters, and then the assessment system estimates the potential influence scope and the severity in the emergency. As show in figure 3, the graph of networks and the data of flaw and pressure can be present with different colors. Moreover, the prioritized protection areas are located based on the data of buildings and sensors embedded beforehand in BIM and GIS. The predicted consequences of the leakage itself along with the states of the neighboring space, determine the level of alarm, disposal procedures and the response team and resource to mobilize. For example, if there are mass gathering or important buildings, such as laboratories, examination rooms and so on, in the neighboring space, the alarm level is certainly higher than the situation that the perimeter zone is open area or similar states. In addition, the amount of leakage is undoubtedly positive correlated to the alarm level. Meanwhile the disposal forces are called together, according to the command issued on the mobile terminals, and then the state of the scene and the corresponding disposal instruction will be distributed with them. The graph of pipe networks, related parameters of the pipe, like flow and pressure etc., the neighboring facilities requiring attention and analogous information are included. When the emergency is handled, the states of all regions come back to the default state, with regular color presented. If the situation gets worse, for example, the leakage cannot be controlled or other secondary incidents come up, another disposal force is going to evacuate the people nearby. What’s more, there is a coordinate response mechanism to call on the more professional disposal team out of hospitals on the basis of the pre-arranged plan stored in the database.