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Using single and multiple unmanned aerial vehicles for microscopic driver behaviour data collection at freeway interchange ramps

- 01 Feb 2022 - 
- Vol. 49, Iss: 2, pp 212-221
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TLDR
In this paper , a detailed methodological framework for collecting microscopic driver and vehicle behaviour data over a long road segment with an application to the entire stretch of a freeway ramp segment using single and multiple unmanned aerial vehicles (UAVs).
Abstract
This paper presents a detailed methodological framework for collecting microscopic driver and vehicle behaviour data over a long road segment with an application to the entire stretch of a freeway ramp segment using single and multiple unmanned aerial vehicles (UAVs). The methodology allows users to collect reliable and complete trajectories of traffic movements in areas with challenging physical characteristics (long road segment, horizontal curvature, changing elevation, and presence of shadow), challenging traffic characteristics (high traffic volume, high speeds, and high-speed changes), and restrictive regulations (UAVs prohibited from hovering over the freeway or the right-of-way). Different UAV setups are recommended and can be used depending on the site conditions. Specific commercial software and procedures used to complete the data collection are explained. The methodology was applied at two ramps and verified with speed data acquired from differential GPS receivers using three different error metrics. The results showed good performance of the proposed methodology, including when aerial videos were taken from oblique angles.

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Journal ArticleDOI

Driver Behavior on Exit Freeway Ramp Terminals Based on the Naturalistic Driving Study

TL;DR: In this paper , the authors used trip data from the SHRP-2 Naturalistic Driving Study (NDS) database collected at 12 sites in three states across the United States to investigate driver behavior at freeway exit ramp terminals.
Journal ArticleDOI

Driver Behavior Performance at Freeway Exit Ramp Terminals: Investigation and Modeling

TL;DR: In this article , the authors investigated drivers' diverging behavior along exit ramp terminal segments, including freeway right lane (FRL), SCL, and ramps based on video-based trajectory data collected using unmanned aerial vehicles (UAVs).
Journal ArticleDOI

Capitalizing on Drone Videos to Calibrate Simulation Models for Signalized Intersections and Roundabouts

TL;DR: In this paper , the authors use drone data to calibrate model parameters pertaining to intersection operation and show how saturation flow rates can be adjusted for signalized intersections so that queue dynamics and delays can be matched.
References
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Innovative Uses of Video Analysis

Douglas Brown, +1 more
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TL;DR: In this article, the authors describe the use of Tracker, a free Java video analysis tool developed by the Open Source Physics Project, to extend video analysis beyond these traditional applications and discuss the following introductory physics video experiments.
Journal ArticleDOI

Detecting and tracking vehicles in traffic by unmanned aerial vehicles

TL;DR: A new vehicle detecting and tracking system based on image data collected by UAV, which exhibits high accuracy in traffic information acquisition at different UAV altitudes with different view scopes, which can be used in future traffic monitoring and control in metropolitan areas.
BookDOI

Naturalistic Driving Study: Technical Coordination and Quality Control

TL;DR: The technical coordination and quality control carried out by the Virginia Tech Transportation Institute (VTTI) for the Strategic Highway Research Program 2 (SHRP 2) Naturalistic Driving Study (NDS) is described in this article.