Analysis Of Traffic Based On Signals Using Different Feature Inputs

Document Type

Conference Proceeding

Publication Date

2021

Publication Title

Information Technology - New Generations. Advances in Intelligent Systems and Computing

First page number:

239

Last page number:

243

Abstract

The Regional Transportation Commission of Southern Nevada (RTC) manages vehicle traffic in Clark County, Nevada. The RTC division that manages the roadway and freeway street signal devices is the division of Freeway Arterial System of Transportation (FAST). They capture and store their maintenance vehicle trucks’ GPS trip information into a publicly available data set. This data set brings a tremendous opportunity to analyze the traffic trends. This study uses machine learning algorithms to solve two types of problems, classification and regression. For classification, we have obtained 65% accuracy using signal types as the target and significantly low accuracy when we use the total stop time as the target variable. For regression, both Random Forest and Support Vector models performed poorly. However, the Random Forest model performed slightly better than the Support Vector model. Altogether these results help us determine the next steps to explore.

Keywords

Pattern recognition and classification; Data sciences; Traffic light coordination

Disciplines

Data Science | United States History

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