Multi-objective optimization using evolutionary algorithms for geographical zone partitioning

2011 2011

Other formats: Order a copy

Abstract (summary)

Transportation planning initiatives at different administrative levels require traffic data at appropriate resolutions. This research presents a methodology for determination of optimal traffic resolution depending on the planning level under consideration. The framework is supported by a proof of concept in the form of a Geographical Zone Partitioning Software (GEZOPS) - a decision-support tool serving to fill an existing gap in the domain of high level traffic analysis tools for transportation planning. The strategy is based on multi-objective optimization using weighted agglomerative ranking & selection and evolutionary algorithms. Facility for handling geospatial constraints for contiguity and primary arterial boundaries is provided with full-scale integration with mapping and software for statistical analysis.

In contributing to the area of evolutionary algorithms, the research demonstrates the application of Restricted Growth Functions (RGF) to encode the set partition. This encoding is used with modern evolutionary algorithms such as the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Problem-specific issues such as geospatial constraints and representation provide the necessary motivation for design and development of diversity preservation strategies, crossover and mutation approaches to provide the necessary balance between exploration and exploitation. Specific algorithmic aspects and parameterization specifications are implemented to provide a platform for sensitivity analysis and comparative studies.

The Huntsville Metropolitan Planning Organization (HMPO) is used as a case study for testing the algorithms developed in this research initiative. Results from subject-matter expert assessment and validation are provided to demonstrate that the strategy produces partitions that are not only quantitatively fit but also useful from a transportation planning perspective. Scope for further research in the zone partitioning problem using other formulations is identified.

Indexing (details)

Industrial engineering;
Transportation planning;
Operations research
0546: Industrial engineering
0709: Transportation planning
0796: Operations research
Identifier / keyword
Social sciences; Applied sciences; Cluster analysis; Data mining; Evolutionary algorithms; Knowledge discovery; Multi-objective optimization; Zone partitioning
Multi-objective optimization using evolutionary algorithms for geographical zone partitioning
Sharma, Nitin S.
Number of pages
Publication year
Degree date
School code
DAI-B 73/05, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
Swain, James J.
The University of Alabama in Huntsville
University location
United States -- Alabama
Source type
Dissertations & Theses
Document type
Dissertation/thesis number
ProQuest document ID
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
Access the complete full text

You can get the full text of this document if it is part of your institution's ProQuest subscription.

Try one of the following:

  • Connect to ProQuest through your library network and search for the document from there.
  • Request the document from your library.
  • Go to the ProQuest login page and enter a ProQuest or My Research username / password.