A data driven method for OD matrix estimation

The fundamental challenge of the origin-destination (OD) matrix estimation problem is that it is severely under-determined. In this paper the authors propose a new data driven OD estimation method for cases where a supply pattern in the form of speeds and flows is available. The authors show that with these input data, they do not require an iterative dynamic network loading procedure that results in an equilibrium assignment, nor do they need an assumption on the kind of equilibrium that emerges from this process. The minimal number of ingredients which are needed are (a) a method to estimate/predict production and attraction time series; (b) a method to compute the N shortest paths from each OD zone to the next; and (c) two—possibly OD-specific—assumptions on the magnitude of N; and on the proportionality of path flows between these origins and destinations, respectively. The latter constitutes the most important behavioral assumption in the authors' method, which relates to how the they assume travelers have chosen their routes between OD pairs. The authors choose a proportionality factor that is inversely proportional to realized travel time, where they incorporate a penalty for path overlap. For large networks, these ingredients may be insufficient to solve the resulting system of equations. The authors show how additional constraints can be derived directly from the data by using principal component analysis, with which they exploit the fact that temporal patterns of production and attraction are similar across the network. Experimental results on a toy network and a large city network (Santander, Spain) show that the authors' OD estimation method works satisfactorily, given a reasonable choice of N, and the use of so-called 3D supply patterns, which provide a compact representation of the supply dynamics over the entire network. Inclusion of topological information makes the method scalable both in terms of network size and for different topologies. Although the authors use a neural network to predict production and attraction in their experiments (which implies ground-truth OD data were needed), there are straight-forward paths to improve the method using additional data, such as demographic data, household survey data, social media and or movement traces, which could support estimating such ground-truth baseline production and attraction patterns. The proposed framework would fit very nicely in an online traffic modeling and control framework, and the authors see many paths to further refine and improve the method.

• Record URL:
  • https://doi.org/10.1016/j.trc.2019.05.014
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0968090X18317388
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2019 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Krishnakumari, Panchamy
  • van Lint, Hans
  • Djukic, Tamara
  • Cats, Oded
  • 0000-0002-4506-0459
• Publication Date: 2020-4

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; References; Tables;
• Pagination: pp 38-56
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 113
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Estimation theory; Networks; Statistical analysis; Trip matrices
• Uncontrolled Terms: Principal component analysis
• Geographic Terms: Santander (Spain)
• Subject Areas: Highways; Planning and Forecasting;

Filing Info

• Accession Number: 01707603
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 6 2019 9:30AM