Abstract
At present, there are various traffic analysis approaches and tools accessible in all areas; nevertheless, there are not enough, or by all-means, resources, and supplies for the application of these tools, as these tools differ in their competencies, input supplies, and productivity. This paper aims to provide a new way for a cost-effective traffic analysis implementation, which does not require a lot of resources, combining two machine learning algorithms to count the vehicles, estimate their speed, and segment lanes from a video recording. The video recording can be done using a conventional mobile phone camera and can be processed using a simple hardware toolkit. To bear out the cost-effectiveness of the proposed procedure, we provide a cost comparison analysis with a radar-based mobile traffic counting device.
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