Abstract
Unmanned Aerial Vehicles (UAVs) represent easy, affordable, and simple solutions for many tasks, including the collection of traffic data. The main aim of this study is to propose a new, low-cost framework for the determination of highly accurate traffic flow parameters. The proposed framework consists of four segments: terrain survey, image processing, vehicle detection, and collection of traffic flow parameters. The testing phase of the framework was done on the Zagreb bypass motorway. A significant part of this study is the integration of the state-of-the-art pre-trained Faster Region-based Convolutional Neural Network (Faster R-CNN) for vehicle detection. Moreover, the study includes detailed explanations about vehicle speed estimation based on the calculation of the Mean Absolute Percentage Error (MAPE). Faster R-CNN was pre-trained on Common Objects in COntext (COCO) images dataset, fine-tuned on 160 images, and tested on 40 images. A dual-frequency Global Navigation Satellite System (GNSS) receiver was used for the determination of spatial resolution. This approach to data collection enables extraction of trajectories for an individual vehicle, which consequently provides a method for microscopic traffic flow parameters in detail analysis. As an example, the trajectories of two vehicles were extracted and the comparison of the driver’s behavior was given by speed—time, speed—space, and space—time diagrams.
Highlights
The number of vehicles in Europe is increasing every year
A significant part of this study is the integration of the state-of-the-art pre-trained Faster Region-based Convolutional Neural Network (Faster R-CNN) for vehicle detection
Particular emphasis was placed on achieving high accuracy. This is done with the Faster R-CNN object detection network, which is pre-trained on the Common Objects in COntext (COCO) dataset
Summary
According to the European Automobile Manufacturers Association (ACEA), in 2019, there were 531 passenger cars per 1000 inhabitants in the European Union (EU) Comparing this number with 497 cars in 2014, it gives a 7% increase over five years [1]. Numerical values describing each road are indispensable and essential for performing a proper analysis This is the main task of traffic engineers, who usually describe the flow using deterministic modeling of traffic flow parameters, depending on their requirements. According to Rao, the macroscopic parameters characterize the traffic as a whole and microscopic parameters study the behavior of an individual vehicle in the flow relating to one another [2]. Collecting accurate and detailed traffic flow data and obtaining parameters at specific locations can be a very expensive, demanding, and time-consuming process
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