Road Traffic State Clustering Using Speed Transition Matrices

A novel approach for classification of the traffic state on a city-wide scale

Leo Tisljaric, PhD
Towards Data Science
5 min readJan 24, 2021

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Photo by Alvaro Reyes on Unsplash

Speed Transition Matrix (STM) is a novel traffic data technique used for all kinds of traffic-related analysis like traffic state estimation, anomaly detection, etc. This article will show how the STM can be applied to traffic state classification problems when dealing with GPS data. The full paper can be found here [1] .

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Intro

Clustering techniques are mostly used to group the unlabeled datasets. When dealing with traffic data, the goal is to cluster data is to groups that follow a similar traffic pattern. With this, the analysis of large traffic datasets is simpler and more interpretable. In this article, we are trying to group the data to represent the different traffic states like “Congestion,” “Unstable flow,” etc. [2].

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Towards Data Science
Towards Data Science

Published in Towards Data Science

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Leo Tisljaric, PhD
Leo Tisljaric, PhD

Written by Leo Tisljaric, PhD

AI practitioner with passion for research and development of new ideas.

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