Title
Unmanned Aerial Vehicle-Based Traffic Analysis: A Case Study for Shockwave Identification and Flow Parameters Estimation at Signalized Intersections.
Abstract
Owing to their dynamic and multidisciplinary characteristics, Unmanned Aerial Vehicles (UAVs), or drones, have become increasingly popular. However, the civil applications of this technology, particularly for traffic data collection and analysis, still need to be thoroughly explored. For this purpose, the authors previously proposed a detailed methodological framework for the automated UAV video processing in order to extract multi-vehicle trajectories at a particular road segment. In this paper, however, the main emphasis is on the comprehensive analysis of vehicle trajectories extracted via a UAV-based video processing framework. An analytical methodology is presented for: (i) the automatic identification of flow states and shockwaves based on processed UAV trajectories, and (ii) the subsequent extraction of various traffic parameters and performance indicators in order to study flow conditions at a signalized intersection. The experimental data to analyze traffic flow conditions was obtained in the city of Sint-Truiden, Belgium. The generation of simplified trajectories, shockwaves, and fundamental diagrams help in analyzing the interrupted-flow conditions at a signalized four-legged intersection using UAV-acquired data. The analysis conducted on such data may serve as a benchmark for the actual traffic-specific applications of the UAV-acquired data. The results reflect the value of flexibility and bird-eye view provided by UAV videos; thereby depicting the overall applicability of the UAV-based traffic analysis system. The future research will mainly focus on further extensions of UAV-based traffic applications.
Year
DOI
Venue
2018
10.3390/rs10030458
REMOTE SENSING
Keywords
Field
DocType
drones,UAVs,traffic data,traffic data collection,traffic flow analysis,vehicle trajectories,shockwave analysis
Computer vision,Data collection,Traffic analysis,Video processing,Performance indicator,Traffic flow,Experimental data,Flow conditions,Real-time computing,Artificial intelligence,Drone,Geology
Journal
Volume
Issue
ISSN
10
3
2072-4292
Citations 
PageRank 
References 
5
0.48
1
Authors
5
Name
Order
Citations
PageRank
Muhammad Arsalan Khan150.81
Wim Ectors283.06
Tom Bellemans37323.16
Davy Janssens423838.08
Geert Wets576667.59