A Network Partitioning Algorithmic Approach for Macroscopic Fundamental Diagram-Based Hierarchical Traffic Network Management

Kang An, Yi Chang Chiu, Xianbiao Hu, Xiaohong Chen

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

The existence of a macroscopic fundamental diagram (MFD) in a network/subnetwork allows one to formulate hierarchical traffic management strategies. In order to achieve this, a robust and efficient network partitioning algorithm is needed. This research aims to create such an algorithm, where distinct MFD properties exist for each respective partition. The proposed four-step network partition approach utilizes the concept of lambda-connectedness and the technique of region growing and, unlike prior studies, can work with partial traffic data. This research brings forth the following contributions: 1) an algorithmic approach that allows for incomplete traffic datasets as an input and 2) an approach that does not require the user to arbitrarily pre-determine the number of necessary subnetworks. The proposed algorithmic approach can intuitively decide on the number of partitions based on the network connectivity and traffic congestion patterns. The proposed approach was implemented and tested on the regional planning network of Tucson/Pima County Arizona, USA. The MFD related statistics for each subnetwork are presented and discussed. Numerical analysis on lambda choice and algorithm sensitivity regarding different data missing ratios were also performed and elaborated.

Original languageEnglish (US)
Pages (from-to)1130-1139
Number of pages10
JournalIEEE Transactions on Intelligent Transportation Systems
Volume19
Issue number4
DOIs
StatePublished - Apr 2018

Keywords

  • Network partitioning
  • hierarchical management strategy
  • lambda-connectedness
  • macroscopic fundamental diagram
  • region growing

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

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