Map-matching algorithm for applications in multimodal transportation network modeling

Kenneth Perrine, Alireza Khani, Natalia Ruiz-Juri

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Generalized Transit Feed Specification (GTFS) files have gained wide acceptance by transit agencies, which now provide them for most major metropolitan areas. The public availability GTFSs combined with the convenience of presenting a standard data representation has promoted the development of numerous applications for their use. Whereas most of these tools are focused on the analysis and utilization of public transportation systems, GTFS data sets are also extremely relevant for the development of multimodal planning models. The use of GTFS data for integrated modeling requires creating a graph of the public transportation network that is consistent with the roadway network. The former is not trivial, given limitations of networks often used for regional planning models and the complexity of the roadway system. A proposed open-source algorithm matches GTFS geographic information to existing planning networks and is also relevant for real-time in-field applications. The methodology is based on maintaining a set of candidate paths connecting successive geographic points. Examples of implementations using traditional planning networks and a network built from crowdsourced OpenStreetMap data are presented. The versatility of the methodology is also demonstrated by using it for matching GPS points from a navigation system. Experimental results suggest that this approach is highly successful even when the underlying roadway network is not complete. The proposed methodology is a promising step toward using novel and inexpensive data sources to facilitate and eventually transform the way that transportation models are built and validated.

Original languageEnglish (US)
Pages (from-to)62-70
Number of pages9
JournalTransportation Research Record
StatePublished - 2015

Bibliographical note

Funding Information:
This research was supported in part by the U.S. Department of Transportation through the Data-Supported Transportation Operations and Planning (D-STOP) Tier 1 University Transportation Center, as well as by the Capital Area Metropolitan Planning Organization and the Texas Department of Transportation.

Publisher Copyright:
© 2015, National Research Council. All rights reserved.


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