hossein aghamohammadi; reyhaneh saeedi; Ali Asghar Alesheikh; Alireza Vafaeinejad
Abstract
Intelligent emergency response during crises refers to the effort to improve the performance of emergency response units. This type of assistance is created using modern technologies, especially the Internet of Things, to enhance service quality, reduce costs, and increase supervision over the emergency ...
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Intelligent emergency response during crises refers to the effort to improve the performance of emergency response units. This type of assistance is created using modern technologies, especially the Internet of Things, to enhance service quality, reduce costs, and increase supervision over the emergency response process. In a new approach to intelligent emergency response, IoT-based routing models are used. These models optimize the emergency response route through the communication between objects and the collection of spatial data, improving the user experience. In this study, a spatial data infrastructure is designed to integrate the system and improve emergency response. The designed portal displays the optimal route from the incident location to the medical center on a map using the desired service and transfers sensor information (such as heart rate, blood oxygen level, blood pressure, thermometer, glucose meter, electroencephalography, and pulmonary function) to the doctor's mobile phone in the ambulance via Bluetooth and shares this information on the portal. Medical centers also determine their priority using an online hierarchical weighting model. In this system, real-time health information of the injured is received through the submission and confirmation from medical centers, and based on this information, the appropriate location for treatment and transfer of the injured person is determined. In a test conducted for this model, an injured person in Tehran Zone 5 on 4/24/1400 at 12:00 noon was accepted. The emergency team arrived at the scene in less than 8 minutes and, after providing initial assistance, sent the real-time health status of the patient to the surrounding medical centers, receiving a confirmation from Omid hospital in the same area, and transferred the injured person to the hospital in 5 minutes via the optimal route. In contrast, in the traditional system, due to the lack of simultaneous use of IoT and spatial data infrastructure, suitable routing algorithms and weighting models, the emergency response process becomes time-consuming, and medical centers are not informed of real-time health information of the injured. Consequently, this research improves the performance of the emergency response system and addresses some of the issues in the traditional system, such as delays in dispatching emergency personnel and patient non-acceptance. Overall, the use of intelligent emergency response methods reduces human, financial, and time costs, provides quicker and better responses to different crises, and allows emergency organizations to make more intelligent decisions regarding resource distribution and human resource allocation using the data collected by geographic information systems and the Internet of Things.
Bahram Moradi Solooshi; Alireza Vafaeinejad; Hossein Aghamohammadi Zanjirabad; ali asghar ale sheikh
Abstract
The rail transport system consists of the interaction of a set of equipment and operations that determine the capability and capacity of a rail system in freight and passenger transport. For this purpose, it is important to calculate the capacity and predict how it will change, and knowing it will be ...
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The rail transport system consists of the interaction of a set of equipment and operations that determine the capability and capacity of a rail system in freight and passenger transport. For this purpose, it is important to calculate the capacity and predict how it will change, and knowing it will be of great help in improving the level of operation of the railway network. There are several methods for calculating capacity that can be used depending on the type of network and how it is used. To calculate the capacity, the capabilities of spatial information systems are used and with the help of a web-based spatial information system, the operational capacity of the rail network is determined in a new way and with more efficiency than conventional methods. For this purpose, a GIS-based environment that is connected to various databases of the Railway Company of the Islamic Republic of Iran, including the travel database, is used and while observing the current capacity of the network, through multivariate linear regression, the capacity of the rail network in It determines the future. The present study, through linear regression, predicts railway capacity in a case study in Iran for the three selected routes and identifies important blocks for investigating the effect of spatial parameters in determining the capacity of the railway network. Slowly Based on the available data of 1996 (extracted from the Railway Spatial Web Service), capacity forecasting was performed in 1997 in the GIS environment. The results showed that the capacity utilization of the selected routes for freight trains was 82%, passenger routes 56%, 62% return and 79% combined routes. Also, the accuracy of model prediction for freight trains is 35% better than passenger trains, which is due to the difference in speed change and maximum speed allowed for these two types of trains, and modeling accuracy is directly related to the type of part. Route (passenger, freight and combined), so in the passenger route, the modeling capacity of passenger trains was approximately 45% more accurate. Similarly, on the freight route, the estimation of the capacity of freight trains was associated with approximately 45% higher accuracy than that of passenger trains.
Bahram Moradi Solooshi; Alireza Vafaei Nezhad; Hossein Aghamohammadi Zanjirabad; Ali Alesheikh
Abstract
The capacity of passenger and goods shipment in a railway network is affected by various parameters. Considering the high cost of railway construction, optimum utilization of capacity in a railway network can help to improve the efficiency of network. Therefore, the purpose of this study is spatial calculating ...
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The capacity of passenger and goods shipment in a railway network is affected by various parameters. Considering the high cost of railway construction, optimum utilization of capacity in a railway network can help to improve the efficiency of network. Therefore, the purpose of this study is spatial calculating the capacity of railway network of Iran based on the effective parameters. In this paper using the transportation data of trains, a spatial software is developed for calculating the capacity of the railway network. Then, the outputs are compared and evaluated with the daily and real time data of the freight trains performance. In the next step, the amount of capacity utilization of each route and the amount of capacity remaining in each route and block is determined. Based on the analysis, the capacities of the selected passenger double-line route from Semnan to Shahrud, freight single-line route from Yazd to Bafgh and combination single-line route from Arak to Dorood were calculated 2.6 for Semnan to Shahrud path and 2.9 for return path, 13.6 and 12.6 (trains pair/day) respectively. Considering the common calculations, the online calculation with ability connection to related databases and the possibility of exchanging spatial web-based services with the different software, can improve the speed and accuracy of the railway network capacity calculation. On the other hand, in the equation of calculating the capacity (Scott equation) used in Iran, it is common that the adjustment coefficient of passenger train is determined by experts of transportation, while in this paper, since the data of each path is accessible, the aforementioned coefficient is calculated by the ratio of passenger trains number to their maximum number during the considered period of time. The outcome coefficient compared with the coefficient determined by experts, and the result was acceptable. Finally, with access to outputs in the GIS environment, the management solutions were proposed for optimum using the remaining capacity, enhancing the capacity in some parts of the network as well as eliminating the bottlenecks of the railway network.
