Abstract
Multirotor unmanned aerial vehicle video observation can obtain accurate information about traffic flow of large areas over extended times. This paper aims to construct an open data test platform for updated traffic data accumulation and traffic simulation model verification by analyzing real time aerial video. Common calibration boards were used to calibrate internal camera parameters and image distortion correction was performed using a high-precision distortion model. To solve external parameters calibration problems, an existing algorithm was improved by adding two sets of orthogonal equations, achieving higher accuracy with only four calibrated points. A simplified algorithm is proposed to calibrate cameras by calculating the relationship between pixel and true length under the camera optical axis perpendicular to road conditions. Aerial video (160 min) from the Shanghai inner ring expressway was collected and real time traffic parameter values were obtained from analyzing and processing the aerial visual data containing spatial, time, velocity, and acceleration data. The results verify that the proposed platform provides a reasonable and objective approach to traffic simulation model verification and improvement. The proposed data platform also offers significant advantages over conventional methods that use historical and outdated data to run poorly calibrated traffic simulation models.
Highlights
Traffic congestion is frequently encountered on ground roads and urban expressways [1]
Almost all traffic simulation models are confronted with the same difficulties, for example, lack of continuous and detailed real time data and lack of frequent updates based on reliable timely data, leading to inaccurate and improperly calibrated traffic simulation models with questionable results [10, 11]
The unmanned aerial vehicle (UAV) platform used for this study was a DJI PHANTOM 2+ with GoPro HERO3+ Black Edition camera
Summary
Traffic congestion is frequently encountered on ground roads and urban expressways [1]. Cellular automata (CA) is a flexible and powerful visualization tool used in urban growth simulations [6,7,8] and has many appealing features, including simulating bottomup dynamics and capturing self-organizing processes [7, 8] These optimization methods require reasonably accurate estimates for the relevant parameters [9]. To offer a reliable way of collecting spatial-temporal data, camera parameter calibration methods and image distortion correction problems are explored under aerial shooting conditions. Collected and traffic simulation models calibrated using the collected data and traffic parameters
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
