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


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.


Civil, Architectural and Environmental Engineering

Keywords and Phrases

Motor transportation; Network management; Regional planning; Algorithm design and analysis; Computational model; Hierarchical management; Lambda-connectedness; Macroscopic fundamental diagram; Network partitioning; Partitioning algorithms; Region growing; Roads; Shape; Traffic congestion; Hierarchical management strategy; Region growing

International Standard Serial Number (ISSN)

1524-9050; 1558-0016

Document Type

Article - Journal

Document Version


File Type





© 2018 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Apr 2018