Exploring the Use of Drones for Conducting Traffic Mobility and Safety Studies

ABSTRACT Advanced traffic data collection methods, including the application of aerial sensors (drones) as traffic data collectors, can provide real-time traffic information more efficiently, effectively, and safely than traditional methods. Traffic trajectory data like vehicles’ coordinates and point timestamps are challenging to obtain at intersections using traditional field survey methods. The coordinates and timestamps crucial in calculating trajectories can be obtained using drones and their particular integrated software. Thus, this study explores the use of unmanned aerial systems (UAS), particularly tethered drones, to obtain traffic parameters for traffic mobility and safety studies at an unsignalized intersection in Tallahassee, Florida. Tethered drones provided more flexibility in heights and angles and collected data over a relatively larger space needed for the proposed approach. Turning movement counts, gap study, speed study, and Level of Service (LOS) analysis for the stated intersection were the traffic studies conducted in this research. The turning movements were counted through ArcGIS Pro. From the drone footages, the gap study followed by the LOS analysis was carried out. A speed algorithm was developed to calculate speed during a speed study. Based on the results, the intersection operates under capacity with LOS B during the time. Also, the results indicated that the through movement traffic tends to slow down as they approach the intersection while south-bound right and east-bound left-turning traffic increase their speeds as they make a turn. Accuracy assessment was done by comparing the drone footages with the results displayed in ArcGIS software. The drone’s data collection was 100% accurate in traffic movement counting and 96% accurate in traffic movement classification. The level of accuracy is sufficient compared to other advanced traffic data collection methods. In this study, safety was assessed by the surrogate safety measures (SSMs). SSMs can be the viable alternatives for locations with insufficient historical data and indicate potential future conflicts between roadway users. The surrogate measures used in this study include the Time to Collision (TTC), Deceleration-based Surrogate Safety Measure (DSSM), and Post-encroachment Time (PET). TTC and DSSM were used for rear-end conflicts, while PET was used to evaluate cross conflicts and other conflicts such as sideswipes. The number of potential conflicts obtained in a one-hour study period was around 20 per 1000 vehicles traversing the intersection. The number of potential conflicts in one non-peak hour may indicate a safety problem associated with the intersection. This study’s findings can help develop appropriate guidelines and recommendations to transportation agencies in evaluating and justifying the feasibility of using tethered drones as safer and cheaper data collection alternatives while significantly improving intersection safety and operations

Multiple UAV systems: a survey

Nowadays, Unmanned Aerial Vehicles (UAVs) are used in many different applications. Using systems of multiple UAVs is the next obvious step in the process of applying this technology for variety of tasks. There are few research works that cover the applications of these systems and they are all highly specialized. The goal of this survey is to fill this gap by providing a generic review on different applications of multiple UAV systems that have been developed in recent years. We also present a nomenclature and architecture taxonomy for these systems. In the end, a discussion on current trends and challenges is provided.This work was funded by the Ministry of Economy, Industryand Competitiveness of Spain under Grant Nos. TRA2016-77012-R and BES-2017-079798Peer ReviewedPostprint (published version

Driver behaviour detection and vehicle rating using multi-UAV coordinated vehicular networks

與國外學者合著 http://ac.els-cdn.com/S002200001630099X/1-s2.0-S002200001630099X-main.pdf?_tid=d232d06c-7b9f-11e7-89b5-00000aab0f01&acdnat=1502131381_9f4060f121562b9feef2411aaba97d3b[[abstract]]Road accidents account for more than 2% of the total deaths across the globe with more than a million deaths each year due to road mishaps and improper traffic management. Traffic management is a major problem faced by modern cities due to a large number of vehicles operating at the same time. One of the major issues for road mishaps is driver's behaviour and skills. Thus, tracking vehicles and analyzing the driver behaviour is required for proper regulation of traffic. Some of the solutions are provided using infrastructure-based vehicular ad hoc networks (VANETs). However, their operations are constrained by the traffic itself, thus, limiting their scope. A new solution by forming VANETs using multiple unmanned aerial vehicles (UAVs) is proposed which is independent of using road side units (RSUs) for communications. The proposed approach is analyzed using simulations as well as real-time dataset

Driver behaviour detection and vehicle rating using multi-UAV coordinated vehicular networks

[[abstract]]Road accidents account for more than 2% of the total deaths across the globe with more than a million deaths each year due to road mishaps and improper traffic management. Traffic management is a major problem faced by modern cities due to a large number of vehicles operating at the same time. One of the major issues for road mishaps is driver's behaviour and skills. Thus, tracking vehicles and analyzing the driver behaviour is required for proper regulation of traffic. Some of the solutions are provided using infrastructure-based vehicular ad hoc networks (VANETs). However, their operations are constrained by the traffic itself, thus, limiting their scope. A new solution by forming VANETs using multiple unmanned aerial vehicles (UAVs) is proposed which is independent of using road side units (RSUs) for communications. The proposed approach is analyzed using simulations as well as real-time dataset