UAV-Based Traffic Analysis: A Universal Guiding Framework Based on Literature Survey

Abstract: The Unmanned Aerial Vehicles (UAVs) commonly also known as drones are considered as one of the most dynamic and multi-dimensional emerging technologies of the modern era. Recently, this technology has found multiple applications in the transportation field as well; ranging from the traffic surveillance applications to the traffic network analysis for the overall improvement of the traffic flow and safety conditions. However, in order to conduct a UAV-based traffic study, an extremely diligent planning and execution is required followed by an optimal data analysis and interpretation procedure. This paper presents a universal guiding framework for ensuring a safe and efficient execution of a UAV-based study. It also explores the analysis steps that follow the execution of a drone flight. The framework based on the existing studies, is classified into the following seven components: (i) scope definition, (ii) flight planning, (iii) flight implementation, (iv) data acquisition, (v) data processing and analysis, (vi) data interpretation and (vii) optimized traffic application. The proposed framework provides a comprehensive guideline for an efficient conduction and completion of a drone-based traffic study. It gives an overview of the management in the context of the hardware and the software entities involved in the process. In this paper, an extensive yet systematic review of the existing traffic-related UAV studies is presented by moulding them in a step-by-step framework. With the significant increase in the number of UAV studies expected in the coming years, this literature review could become a useful resource for future researchers. The future research will mainly focus on the practical applications of the proposed guiding framework of the UAV-based traffic monitoring and analysis study.

[1]  Eleni I. Vlahogianni,et al.  Computational Intelligence and Optimization for Transportation Big Data: Challenges and Opportunities , 2015 .

[2]  Jun Zhang,et al.  Vehicle flow detection in real-time airborne traffic surveillance system: , 2011 .

[3]  Anh Vu,et al.  A Novel Omni-Directional Vision Sensing Technique for Traffic Surveillance , 2007, 2007 IEEE Intelligent Transportation Systems Conference.

[4]  Konstantinos Kanistras,et al.  A survey of unmanned aerial vehicles (UAVs) for traffic monitoring , 2013, 2013 International Conference on Unmanned Aircraft Systems (ICUAS).

[5]  Deyun Xiao,et al.  Review on vehicle detection based on video for traffic surveillance , 2008, 2008 IEEE International Conference on Automation and Logistics.

[6]  Moshe Ben-Akiva,et al.  Automatic Vehicle Trajectory Extraction by Aerial Remote Sensing , 2014 .

[7]  K.P. Valavanis,et al.  Statistical profile generation for traffic monitoring using real-time UAV based video data , 2007, 2007 Mediterranean Conference on Control & Automation.

[8]  Собкин Владимир Самуилович,et al.  Особенности отношения родителей к школе в зарубежных исследованиях (по материалам публикаций в журнале Social and Behavioral Sciences за 2011-2015 гг. ) , 2016 .

[9]  Eleni I. Vlahogianni,et al.  Extracting Kinematic Characteristics from Unmanned Aerial Vehicles , 2016 .

[10]  Michael Cramer Genauigkeitsuntersuchungen zur GPS/INS-Integration in der Aerophotogrammetrie , 2001 .

[11]  Giuseppe Salvo,et al.  Urban traffic analysis through an UAV , 2014 .

[12]  Mohan Malkani,et al.  Smart video surveillance for airborne platforms , 2009, Robotica.

[13]  B. Coifman,et al.  Surface Transportation Surveillance from Unmanned Aerial Vehicles , 2003 .

[14]  Mark R. McCord,et al.  Roadway traffic monitoring from an unmanned aerial vehicle , 2006 .

[15]  Adam Babinec,et al.  Automatic Vehicle Trajectory Extraction for Traffic Analysis from Aerial Video Data , 2015 .

[16]  Giuseppe Salvo,et al.  Gap acceptance analysis in an urban intersection through a video acquired by an UAV , 2014 .

[17]  P. G. Michalopoulos,et al.  Vehicle detection video through image processing: the Autoscope system , 1991 .

[18]  Patrick Doherty,et al.  From images to traffic behavior - A UAV tracking and monitoring application , 2007, 2007 10th International Conference on Information Fusion.

[19]  Hyondong Oh,et al.  Behaviour recognition of ground vehicle using airborne monitoring of unmanned aerial vehicles , 2014, Int. J. Syst. Sci..

[20]  Jaysen A Yochim THE VULNERABILITIES OF UNMANNED AIRCRAFT SYSTEM COMMON DATA LINKS TO ELECTRONIC ATTACK , 2010 .

[21]  Kang Li,et al.  Driving-Behavior Monitoring Using an Unmanned Aircraft System (UAS) , 2015, HCI.