Agent-Based Simulation and Modeling of Air Transportation System Using GPS
Mohammad Akhondi*
* Corresponding author : Lecturer, Department of Civil and Architectural Engineering, Faculty of Shahid mohajer, Isfahan Branch, Technical and
Vocational University (TVU), Isfahan, Iran.
Email : [email protected]
Abstract
The air transportation and air traffic management system has many complexities as regards the number of the principal elements and parameters and its highly dynamic and variable environment. Moreover, the intelligent agents technology is highly capable of modeling and simulating complex systems and dynamic and variable environments. Therefore, given the great capacities of the intelligent agents, these agents have been recently used to model the transportation systems including the air transportation systems. In this study, agent-based modeling was carried out to simulate the development of an instantaneous information access and control system for the air transportation network considering the presence of GPS systems in all airplanes. Therefore, in the modeling phase, the information on all airplanes, i.e. the mobile agents, or one airplane, including their position, velocity, and instantaneous direction/orientation was available to the user in the modeling phase. Furthermore, finding the best airport quickly for an emergency landing was a goal and it was simulated. Hence, basic modeling of the air transportation system was carried out in the present study to effectively provide for more comprehensive modeling procedures such as the modeling of air traffic control and management systems.
Keywords: intelligent agent, air transportation system, emergency landing, global positioning system, airport.
1.Introduction
The air transportation and air traffic management systems suit agent-based simulation by virtue of having certain characteristics and features. Some of these features are as follows.
- The presence of numerous agents with different roles such as airports, airplanes, and system complexity
- Being goal-oriented
- The agents’ ability to interact in the environment
The first characteristic of the air transportation systems introduces them as systems wherein the agents function to create the system macro-behaviors. The second characteristic suggests that the system designers need to control the micro-behaviors such as the pilots’ behaviors to attain the major management and control goals. The third characteristic stresses the surrounding environment and the interactions among the agents in this environment [1].
Furthermore, agent-based calculations are among the most powerful technologies for the development of distributed complex systems. Many researchers believe that intelligent agents are extremely important models for software development second only to the object-oriented design. They argue that the notion of intelligent agents has a wide and diverse range of applications in the industry, instantaneous control systems, electronic commerce, network management, intelligent transportation systems, information management, and scientific calculations [2].
The intelligent agents technology can improve the ability of the core information systems to cooperate, interact, and carry out distributed calculations particularly through simulation and scenario development [3]. Numerous studies have been conducted on the international level on the application of agent-based modeling to simulation and management in the air transportation industry. Concerning the air traffic management systems, Georgian researchers have introduced the agent-based simulation as the method for forecasting the effect of revolutionary changes in air transportation systems. In this article, agent-based modeling and suitable methods of simulating the air traffic systems are discussed [1].
Greeder et al. (2013) carried out the simulation and modeling of the air transportation industry. In this study, multi-agent modeling and simulation were carried out to analyze and forecast the behavior of the agents and actors in the air transportation system to reduce the travel time in average-distance trips [4]. Meanwhile, Soufian et al. carried out the simulation and modeling of the newly emerging behaviors in the air transportation industry of the Netherlands. Their goal was to demonstrate that agent-based modeling and simulation can help identify the newly emerging, rare and unpredictable behaviors and improve the safety of this complex sociotechnical system. Their simulation results proved that agent-based modeling and simulation of an air transportation system help identify the sudden and newly emerging behaviors, which is not possible or is highly costly with the old simulation methods that are not agent-based (such as human and operational simulations). In fact, the current warning systems do not effectively diminish safety risks [5].
Louis et al. (2018) proposed an agent-based modeling procedure for compensating the delays in Portuguese airlines. In this modeling, an agent-based electronic market is introduced to allow the airlines control center to make sure all flights are made according to schedule or to come up with an acceptable solution to the possible problems by affecting the airline operations and costs very slightly. In fact, an electronic market is designed as a multi-agent system to allow airline companies to negotiate and mobilize the resources [6]. In his 2016 thesis, Marguerite carried out an agent-based simulation of the travel demand in the local air transportation system of Iceland. There is an airport at the center of Reykjavik City, whose authorities formulated their airport development plan based on the release of the valuable lands in the near future, i.e. by 2024. Therefore, a part of the flights had to be transferred to another airport. However, the current location of the airport highly suited most local flight customer. The transfer of the services to Keflavik airport, which was the only alternative, was expected to considerably reduce the number of users of the local flight services due to the increased travel time and costs. The
following the transfer of the flights to the alternative airport. The simulation model results also indicated that the agent-based simulation is a suitable method of estimating the demand variations in the air transportation industry [7]. Raol et al. (2018) performed an agent-based simulation of Urban Air Mobility (UAM). They pointed out that although a new generation of Electric Vertical Take-off and Landing (eVTOL) systems and Personal Air Vehicles (PAV) has attracted global attention, few studies have been carried out to model their integration into an urban transportation system. Therefore, they proposed a basic method for developing the simulation of urban air mobility in the form of a multi- agent transportation simulation. In fact, they presented the first performance analysis of the urban transportation system with regard to the variations of the features of air vehicles and their new take-off and landing infrastructure [8]. In 2012, researchers adopted an agent-based approach to model and assess the performance of airports. Since an airport is a highly complex system for having numerous parameters that determine its performance, uncertainty, and unpredictability, they developed an agent-based model to analyze the behavior and approaches of airports with regard to the principal parameters and elements and the interaction between them [9].
It was decided to utilize the intelligent agent capacities in the dynamic and variable environment of air transportation in this research given our discussion of the air transportation problems, issues, and complexities, especially in the field of air traffic, and considering the modeling and simulation capacities and applications of the agent-based systems. In this study, it was tried to model and simulate the movement of airplanes and access their instantaneous information such as their instantaneous velocity and position, direction, and instantaneous motion azimuth by installing a GPS (Global Positioning System) system in all of them. Similarly, it was tried to show the instantaneous distance of all mobile agents, which are airplanes, from a given city or point. In the model implemented, the rapid accessibility and positioning of the best airplanes were predicted and simulated for the emergency landing of each airplane. This simulation and modeling process primarily sought to simulate and model the air transportation system and simulate the instantaneous information access and management system for the air transportation network using the significant capacities of agent-based simulations. Another goal pursued in this research was to quickly position the best airport for landing during accidents considering the aforesaid information infrastructure. The other goal was to access the information on the airport maps and positions. Finally, the considerable capacities of Anylogic simulation software were used for modeling and simulation purposes.
2. Materials and Methods 2.1 Research Data
The map used as the environment for the movement of the mobile agents and airplanes is a shapefile (.shp) map of Iran that shows the latitude and longitude of the specified areas in Iran. In this environment, some of the Iranian airports are identified based on their longitude and latitude information. Figure (1) depicts the map and the positions of some of the Iranian airports.
Figure (1): Map of Iran in the shapefile format and the positions of the airports As seen in the figure above, the positions of all Iranian airports are marked on this map using the Space Markup option in Anylogic in the GIS section and the GIS Point tool based on the longitude and latitude data of the top airports in Iran.
2.2 Agents
Considering the goal mentioned in the previous section, an air transportation system consisting of two agent types is defined in this study. The agents are as follows.
1, The main agent: It functions as the information collection and control center for the air transportation system
2, Mobile agent: the airplanes
2.2.1 The Main Agent as the Information Collection and Control Center for the Air Transportation System
The main agent defined in Anylogic software plays a pivotal role. In fact, it creates the backbone of the simulation system. In this software, whenever a new model is created, this main agent is created as the first and most important agent by default. In this section, it is possible to add different sections to the simulation environment, add different icons and functions, and create a user interface for the execution of the commands even during the simulation procedure. For instance, it is possible to make graphic changes, add icons to start or finish the simulation, add sections for the display of instantaneous information on the mobile agents (including the velocity, position, or instantaneous direction/orientation of the airplanes), and add display facilitating features (such as zooming and panning features) that are useful during the simulation, etc. in the main agent development environment. Coding was carried out in this powerful simulation software in Java and the powerful class libraries in this software were written in Java.
Figure (2) depicts the main agent development environment created in the software and the added sections.
Figure (2): The main agent development environment
As seen in this figure, different components such as the functions and icons are added to the basic main agent environment in the software and codes are written in Java to define their functions during the simulation process.
2.2.2 Mobile Agents as Airplanes
2.2.2.1 Simulating the GPS System for Airplanes
A GPS simulation system is implemented in this software to simulate the presence of the GPS system in all airplanes. Given the non-GIS and nongraphic environment of the software and the vector simulation carried out in the simulation software or platforms such as JADE (Java Agent Development Framework), the length of the motion vector has to be added to the current position considering the direction of the mobile agent to calculate the instantaneous position and simulate the presence of the GPS system in all agents [10]. However, since it is possible to add the GIS environment in Anylogic, the instantaneous positions of the mobile agents are accessed easily using simple codes.
2.2.2.2 Adding Mobile Agents or Airplanes to the Environment and Simulating Their Motion
Codes were written to add the desired number of mobile agents as airplanes to the simulation environment. To create the mobile agents, an area at a specific latitude and longitude was defined to include Iran. Regardless of the number of mobile agents, they are placed randomly in this area in a specific part of Iran at the time of simulation and at the beginning of the process. When the user checks the tick box in the user interface, the agent motion function is run and the agents start moving randomly in the simulation environment at a predetermined velocity in the motion direction. The placement of the agents in Iran and simulation of their motion were the goals of this section to simulate the other system features such as accessing their instantaneous information and positioning the best airport in a state of emergency. The process of adding airplanes to the simulation environment and their movement in the environment is illustrated in Figure (3).
Figure (3): Adding airplanes to the environment and simulating their movement
As seen in this figure, the airplanes (the mobile agents) are moving in the simulation environment with their unique numbers.
2.2.2.3 Simulating the Airplanes Instantaneous Information Control and Access Center
As mentioned in section (1), one of the important goals pursued in this study was to simulate an instantaneous information access and management system for the air transportation network using the great capacities of the agent-based simulation approach.
Therefore, two features were defined in the simulation environment.
1. By defining a feature in the modeling process, all airplanes or mobile agents present information other than their unique numbers such as their instantaneous velocity, the motion direction or azimuth, and the instantaneous distance from each given point considering its longitude and latitude. The aforesaid point can be selected by the user in the simulation environment and the aforementioned distance is shown as the distance from the destination. The information on the mobile agents in motion is listed in Figure (4).
Figure (4): The detailed information on the mobile agents in the simulation environment
2. Besides, the user is enabled to access more information on each airplane (such as its instantaneous coordinates including its latitude and longitude) and reduce the crowdedness of the simulation environment by clicking on each airplane when the model is run to select the desired airplane and see its instantaneous information.
Figure (5) presents the instantaneous information of an agent selected by the user.
Every airplane that is selected by the user turns red and its information is displayed.
Figure (5): The instantaneous information on the airplanes selected by the user (airplane no. 8 that is marked in red)
As seen in this figure, airplane no. 8 is selected by the user and its instantaneous information including its instantaneous velocity and position, its direction, and its motion status is shown. This information reveals whether the airplane is in motion.
3. It is also possible to select any airplane and click on any desired area in Iran to make the selected airplane switch to the selected area and consider it its destination.
2.2.2.4 Simulating the Search for the Best Airplane for Emergency Landings
As mentioned in section 1, this study also strives for modeling and simulating the emergency landing of airplanes and finding the best solution for their emergency landings. Since all mobile agents or airplanes are equipped with GPS systems, they are aware of their instantaneous positions. Therefore, they select the best airport, which is the nearest airport in this study, for an emergency landing and move towards it based on the coordinates of all airports in Iran. A section is added to the main agent development environment to enable the user to activate the emergency landing system. When this system is activated, if the user selects an airplane during the simulation, the airplanes switch to the emergency landing mode and move towards the nearest airplane. The process of finding the best airport is expressed via relation (1).
D (Airplane(i)) = Airport (j) that d(ij) = min (1)
Where D denotes the airplane destination, Airplane (i) represents the i-th mobile agent or airplane, Airport (j) is the j-th airport, and d(ij) refers to the distance between the i-th airplane and the j-th airport.
Figure (6) presents The process of selection of an airplane by the user and movement of the airplane towards the destination for an emergency landing.
As seen in this figure, the “emergency landing” option is also added to the simulation environment. Therefore, after selecting this option, every airplane selected by the user turns blue, selects the nearest airport for an emergency landing after calculating its instantaneous distance from all airports, and moves towards the selected airport. Here, airplane no. 21 is moving towards Yazd Airport, which is the nearest airport at the
162 azimuth and at a speed of 149m/s for an emergency landing.
3. Conclusion
In this study, the simulation and modeling of the air transportation system were carried out to simulate an instantaneous information access and management system for the air transportation network using the considerable capacities of agent-based simulation.
Another research goal was to quickly position the best airport for the emergency landing of airplanes during an accident by creating the required information infrastructure and using the airport position information and maps. In the model implemented in this study, access was granted to the instantaneous information system of the air transportation network. Information such as the instantaneous velocity, instantaneous position, and motion direction, which is the most important information on one of the principal components of the air transportation systems, i.e. the airplanes, is easily available to the users along with the information on the airport positions. The user can also easily simulate an emergency landing by selecting an airplane and switching to the emergency landing mode. In fact, the present study set the scene for more comprehensive analyses of the air transportation systems. Besides, the capacities of the powerful Anylogic simulation software and platform and its class library were analyzed for the simulation and modeling of the air transportation network. Graphical features and a useful user interface were also defined in the modeling process. Finally, suggestions for completing and developing the model in the future are presented hereunder.
- As stated, accessing the instantaneous information of the air transportation system was made feasible in this study. Through more comprehensive analyses and model development, it is possible to use the created information system and platform in the development of scenarios and analysis of different methods for air transportation network traffic management.
- In the modeling process, the primary goals were providing access to the instantaneous information of the airplanes, locating the best airport for an emergency landing, and analyzing the capacities of the Anylogic platform. Therefore, accessing the real air transportation network information such as the weekly flights of airports and the actual flight path of the airplanes was overlooked. Therefore, real information can be added in the future to develop the model for actual analyses.
- Only the distance parameter or the distance from the nearest airport was taken into account in the process of finding the best airport for an emergency landing. Perhaps it is the most important parameter. However, other parameters such as the airports status and features and their capacities for accepting emergency landing requests based on the airplane size, air traffic, and weather conditions can be taken into account in the model development phase to develop a more realistic model.
4. References
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