Cluster Lifecycle Analysis: Challenges, Techniques, and Framework

TL;DR

This paper explores the lifecycle of clusters in moving object data, proposing a framework to analyze their genesis, existence, and disappearance, which can enhance transportation data analysis and predictive capabilities.

Contribution

It introduces a novel focus on cluster lifecycle analysis and a big data framework for managing cluster identification and processing in transportation data.

Findings

Proposes a framework for cluster lifecycle management

Highlights potential for improved transportation analysis

Lays groundwork for future predictive models

Abstract

Novel forms of data analysis methods have emerged as a significant research direction in the transportation domain. These methods can potentially help to improve our understanding of the dynamic flows of vehicles, people, and goods. Understanding these dynamics has economic and social consequences, which can improve the quality of life locally or worldwide. Aiming at this objective, a significant amount of research has focused on clustering moving objects to address problems in many domains, including the transportation, health, and environment. However, previous research has not investigated the lifecycle of a cluster, including cluster genesis, existence, and disappearance. The representation and analysis of cluster lifecycles can create novel avenues for research, result in new insights for analyses, and allow unique forms of prediction. This technical report focuses on studying the lifecycle of clusters by investigating the relations that a cluster has with moving elements and other clusters. This technical report also proposes a big data framework that manages the identification and processing of a cluster lifecycle. The ongoing research approach will lead to new ways to perform cluster analysis and advance the state of the art by leading to new insights related to cluster lifecycle. These results can have a significant impact on transport industry data science applications in a wide variety of areas, including congestion management, resource optimization, and hotspot management.