DeCoClu: Density consensus clustering approach for public transport data

Research output: Contribution to journalArticle

Abstract

Automatic Vehicle Monitoring (AVM) systems are exploited by public transport companies to manage and control their fleet of vehicles. However, these systems are usually based on the background knowledge of the transport network which can change during the time and in some cases can be missing or erroneous. GPS data and other information captured by the vehicles during their work can be exploited to update the network knowledge. This paper presents a novel approach, namely DeCoClu (Density Consensus Clustering), that aims at mining the topology of a public transport network by means of a consensus clustering density-based approach. In particular, the method exploits static information from time series of positioning signals (i.e., GPS data) to infer geographical locations of stops by means of a consensus clustering strategy based on a new distance function. Moreover, the logical pathway of a route (i.e., stops sequence) is defined by an Hamiltonian cycle. Experiments performed on real-data collections provided by a public transport company demonstrate the effectiveness of the proposed approach.

Original languageEnglish
Pages (from-to)378-388
Number of pages11
JournalInformation Sciences
Volume328
DOIs
Publication statusPublished - Jan 20 2016

Fingerprint

Clustering
Global positioning system
Hamiltonians
Time series
Hamiltonian circuit
Industry
Distance Function
Monitoring System
Topology
Positioning
Mining
Pathway
Monitoring
Update
Public transport
Experiments
Demonstrate
Experiment
Knowledge
Monitoring system

Keywords

  • Consensus clustering
  • Density-based clustering
  • GPS data
  • Public transport data

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management

Cite this

DeCoClu : Density consensus clustering approach for public transport data. / Fiori, Alessandro; Mignone, Andrea; Rospo, Giuseppe.

In: Information Sciences, Vol. 328, 20.01.2016, p. 378-388.

Research output: Contribution to journalArticle

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