Unmanned Aerial Aircraft Systems for transportation engineering: Current practice and future challenges

Barmpounakis, Emmanouil; Vlahogianni, Eleni I.; Golias, John

doi:10.1016/j.ijtst.2017.02.001

Cited by 184 publications

(86 citation statements)

References 54 publications

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“…Another scientific report provided a summary of drone applications for managing transportation [19]. The authors focused on the theory and practice of UAS in transportation and traffic engineering.…”

Section: Related Workmentioning

confidence: 99%

Developing Models for Managing Drones in the Transportation System in Smart Cities

Nguyen

2019

Electrical, Control and Communication Engineering

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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.

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Section: Related Workmentioning

confidence: 99%

Developing Models for Managing Drones in the Transportation System in Smart Cities

Nguyen

2019

Electrical, Control and Communication Engineering

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“…Based on online searching, scientific publications related to TCT conducted in Indonesia are only found in limited numbers [14][15][16][17][18][19][20][21][22][23]. In the search actually found also some Indonesian researchers who conducted research related to traffic conflict on abroad, for example, conducted by Malkhamah et al [24] or Suwarto and Basuki [25]. Because this article focuses on traffic conflict studies conducted in Indonesia, those studies are not in the discussion of this article.…”

Section: Traffic Conflict Studies In Indonesiamentioning

confidence: 99%

“…UAVs are aircraft that are controlled by pilots on land or by an electronic system [25]. It has been used in various fields, both for military and civilian purposes.…”

Section: The Challenges For Traffic Conflict Studies In Indonesiamentioning

confidence: 99%

“…As a traffic data collection tool, UAV was used in research conducted by Barmpounakis et al [25], Coifman et al [26], and Salvo et al [27]. The use of UAVs in traffic surveys has the advantage of providing cost-effective "eye in the sky" solutions [25]. In addition, UAV also has no effect on driver behavior [27].…”

Section: The Challenges For Traffic Conflict Studies In Indonesiamentioning

confidence: 99%

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A brief review: traffic conflict techniques and the challenges of the studies in Indonesia

Kusumastutie

Rusmandani

2019

MATEC Web Conf.

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For the past 50 years, Traffic Conflict Technique (TCT) has been growing rapidly as Surrogate Safety Measure (SSM). Unfortunately, the study of TCT as a road safety analysis tool in Indonesia is still limited. This article aims to describe the developments of TCT, regarding to the use of manual observation, automated video analysis and simulations with the Surrogate Safety Assessment Model (SSAM). This article also aims to identify the challenges of TCT implementation in improving road safety in Indonesia. Thus, it is expected to inspire researchers in Indonesia to develop TCT, for example by using Unmanned Aerial Vehicle (UAV) and microsimulation in TCT studies.

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“…The unmanned aerial vehicles (UAVs) technology has continuously been evolving with exceptional growth over the last years [1], leading to the emergence of a large number of services offered and potential applications. Drones are not meant to only serve with military purposes [2], but have also become widely used in civilian and industrial domain such us logistics and transportation [3][4][5], photography and filmmaking [6], safety and security [7], mapping [8], agriculture [9,10], monitoring [11][12][13][14], surveillance [15][16][17], architecture [18][19][20] and others applications. They were originally used for missions tedious or too dangerous for humans and all this due to their ease of deployment, low maintenance cost [21], high mobility and hovering capability [22].…”

Section: Introductionmentioning

confidence: 99%

Optimization of ANFIS controllers using improved ant colony to control an UAV trajectory tracking task

2020

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Development of unmanned aerial vehicles (UAVs) has become the most important research areas in the field of autonomous aeronautical control. This paper proposes a robust and intelligent controller based on adaptive-network-based fuzzy inference system (ANFIS) and improved ant colony optimization (IACO) to govern the behavior of a three degree of freedom quadrotor UAV. The quadrotor was chosen due to its simple mechanical structure; nevertheless, these types of aircraft are highly nonlinear. Intelligent control such as fuzzy logic is a suitable choice for controlling nonlinear systems. The ANFIS controller is used to reproduce the desired trajectory of the quadrotor in 2D Vertical plane and the IACO algorithm aims is to facilitate convergence to the ANFIS's optimal parameters in order to reduce learning errors and improve the quality of the controller. To evaluate the performance of the proposed IACO tuned ANFIS controller, a comparison between the proposed ANFIS-IACO controller and other controller's performance such us ANFIS only and proportional-integral-derivative controllers is illustrated using the same system. As expected, the hybrid ANFIS-IACO controller gives very satisfactory results than the others methods already developed in the same study.

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Unmanned Aerial Aircraft Systems for transportation engineering: Current practice and future challenges

Cited by 184 publications

References 54 publications

Developing Models for Managing Drones in the Transportation System in Smart Cities

Developing Models for Managing Drones in the Transportation System in Smart Cities

A brief review: traffic conflict techniques and the challenges of the studies in Indonesia

Optimization of ANFIS controllers using improved ant colony to control an UAV trajectory tracking task

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