Clustering of Urban Traffic Patterns by K-Means and Dynamic Time Warping: Case Study

TL;DR

This paper presents a clustering method using K-Means and Dynamic Time Warping to analyze urban traffic patterns, enabling missing data estimation and essential road segment identification based on speed time series.

Contribution

The paper introduces a novel application of K-Means and Dynamic Time Warping for clustering urban traffic patterns, demonstrated on real driver speed data.

Findings

Effective clustering of traffic patterns achieved

Missing speed data can be estimated accurately

Identification of key road segments for mapping

Abstract

Clustering of urban traffic patterns is an essential task in many different areas of traffic management and planning. In this paper, two significant applications in the clustering of urban traffic patterns are described. The first application estimates the missing speed values using the speed of road segments with similar traffic patterns to colorify map tiles. The second one is the estimation of essential road segments for generating addresses for a local point on the map, using the similarity patterns of different road segments. The speed time series extracts the traffic pattern in different road segments. In this paper, we proposed the time series clustering algorithm based on K-Means and Dynamic Time Warping. The case study of our proposed algorithm is based on the Snapp application's driver speed time series data. The results of the two applications illustrate that the proposed method can extract similar urban traffic patterns.