A generalized partite-graph method for transportation data association
There are many problems in transportation which involve reconstructing the associations between different entities. For example, data points related to a vehicle from different sensors could be matched to reconstruct the trajectories of vehicles. Or, in population synthesis for microsimulation, lists of persons, dwellings, and vehicles could be generated individually from source data and then matched into synthetic households. There are numerous other examples. The unifying theme is a desire to construct realistic unit-level associations from aggregate or anonymized data. The problem demands a method that is behaviorally consistent and operationally efficient to handle large datasets. The authors adapt concepts from graph theory to formulate this class of problems as a k-partite graph. This approach is generic and can incorporate expectations of behavior in the form of edge weights. A Dijkstra algorithm based solution is proposed for a subset of k-partite graphs which permits a direct comparison with pair-wise matching and applied to a case study of bicycle tracklets. The authors then propose an iterative improvement algorithm as a generic method and apply it to a complete k-partite graph in a population synthesis case study. The first case study shows that the k-partite algorithm outperforms the previously used pair-wise matching algorithms. The second case study demonstrates the generality of the proposed algorithm to all k-partite graphs and shows that the generic method is fast and scalable to large problems. As a whole, this paper aims to show that k-partite methods are behaviorally consistent, efficient, and potentially applicable to a wide variety of transportation data association problems.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
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Supplemental Notes:
- Abstract reprinted with permission of Elsevier.
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Authors:
- Anderson, Paul
- Farooq, Bilal
- Publication Date: 2017-3
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References; Tables;
- Pagination: pp 150-169
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Serial:
- Transportation Research Part C: Emerging Technologies
- Volume: 76
- Publisher: Elsevier
- ISSN: 0968-090X
- Serial URL: http://www.sciencedirect.com/science/journal/0968090X
Subject/Index Terms
- TRT Terms: Algorithms; Data analysis; Data files; Data management; Graph theory; Trajectory; Vehicles
- Uncontrolled Terms: Data association techniques
- Subject Areas: Data and Information Technology; Highways; Planning and Forecasting;
Filing Info
- Accession Number: 01631647
- Record Type: Publication
- Files: TRIS
- Created Date: Mar 31 2017 5:09PM