Developing Models for Managing Drones in the Transportation System in Smart Cities
Unmanned aerial vehicles (UAVs), especially drones, have advantages of having applications in different areas, including agriculture, transportation, such as land use surveys and traffic surveillance, and weather research. Many network protocols are architected for the communication between multiple...
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| Format: | Article |
| Language: | English |
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Sciendo
2019-12-01
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| Series: | Electrical, Control and Communication Engineering |
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| Online Access: | https://doi.org/10.2478/ecce-2019-0010 |
| _version_ | 1818192004578803712 |
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| author | Dung Nguyen Dinh |
| author_facet | Dung Nguyen Dinh |
| author_sort | Dung Nguyen Dinh |
| collection | DOAJ |
| description | Unmanned aerial vehicles (UAVs), especially drones, have advantages of having applications in different areas, including agriculture, transportation, such as land use surveys and traffic surveillance, and weather research. Many network protocols are architected for the communication between multiple drones. The present study proposes drone-following models for managing drones in the transportation management system in smart cities. These models are based on the initial idea that drones flight towards a leading drone in the traffic flow. Such models are described by the relative distance and velocity functions. Two types of drone-following models are presented in the study. The first model is a safe distance model (SD model), in which a safe distance between a drone and its ahead is maintained. By applying the stochastic diffusion process, an improved model, called Markov model, is deduced. These drone-following models are simulated in a 2D environment using numerical simulation techniques. With the simulation results, it could be noted that: i) there is no accident and no unrealistic deceleration; ii) the velocity of the followed drone is changed according to the speed of the drone ahead; iii) the followed drones keep a safe distance to drone ahead even the velocities are changed; iv) the performance of the Markov model is better than that of the SD model. |
| first_indexed | 2024-12-12T00:23:37Z |
| format | Article |
| id | doaj.art-0fa34f71d8754c30b8157378bb510e6d |
| institution | Directory Open Access Journal |
| issn | 2255-9159 |
| language | English |
| last_indexed | 2024-12-12T00:23:37Z |
| publishDate | 2019-12-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Electrical, Control and Communication Engineering |
| spelling | doaj.art-0fa34f71d8754c30b8157378bb510e6d2022-12-22T00:44:41ZengSciendoElectrical, Control and Communication Engineering2255-91592019-12-01152717810.2478/ecce-2019-0010ecce-2019-0010Developing Models for Managing Drones in the Transportation System in Smart CitiesDung Nguyen Dinh0Ph.D. student, Department of Aeronautics, Naval Architecture and Railway Vehicles, Budapest University of Technology and Economics, HungaryUnmanned aerial vehicles (UAVs), especially drones, have advantages of having applications in different areas, including agriculture, transportation, such as land use surveys and traffic surveillance, and weather research. Many network protocols are architected for the communication between multiple drones. The present study proposes drone-following models for managing drones in the transportation management system in smart cities. These models are based on the initial idea that drones flight towards a leading drone in the traffic flow. Such models are described by the relative distance and velocity functions. Two types of drone-following models are presented in the study. The first model is a safe distance model (SD model), in which a safe distance between a drone and its ahead is maintained. By applying the stochastic diffusion process, an improved model, called Markov model, is deduced. These drone-following models are simulated in a 2D environment using numerical simulation techniques. With the simulation results, it could be noted that: i) there is no accident and no unrealistic deceleration; ii) the velocity of the followed drone is changed according to the speed of the drone ahead; iii) the followed drones keep a safe distance to drone ahead even the velocities are changed; iv) the performance of the Markov model is better than that of the SD model.https://doi.org/10.2478/ecce-2019-0010air transportationmathematical modelpath planningsafety managementvehicle routing |
| spellingShingle | Dung Nguyen Dinh Developing Models for Managing Drones in the Transportation System in Smart Cities Electrical, Control and Communication Engineering air transportation mathematical model path planning safety management vehicle routing |
| title | Developing Models for Managing Drones in the Transportation System in Smart Cities |
| title_full | Developing Models for Managing Drones in the Transportation System in Smart Cities |
| title_fullStr | Developing Models for Managing Drones in the Transportation System in Smart Cities |
| title_full_unstemmed | Developing Models for Managing Drones in the Transportation System in Smart Cities |
| title_short | Developing Models for Managing Drones in the Transportation System in Smart Cities |
| title_sort | developing models for managing drones in the transportation system in smart cities |
| topic | air transportation mathematical model path planning safety management vehicle routing |
| url | https://doi.org/10.2478/ecce-2019-0010 |
| work_keys_str_mv | AT dungnguyendinh developingmodelsformanagingdronesinthetransportationsysteminsmartcities |