GIS-based Map-matching: Development and Demonstration of a Postprocessing Map-matching Algorithm for Transportation Research

This paper presents a GIS-based map-matching algorithm that makes use of geometric, buffer, and network functions in a GIS - to illustrate the suitability of a GIS platform in developing a postprocessing map-matching algorithm for transportation research applications such as route choice analysis. This algorithm was tested using a GPS-assisted time-use survey that involved nearly 2,000 households in Halifax, Nova Scotia, Canada. Actual routes taken by household members who travelled to work by car were extracted using the GPS data and the GIS-based map-matching algorithm. The algorithm produced accurate results in a reasonable amount of time. The algorithm also generated relevant route attributes such as travel time, travel distance, and number of left and right turns that serve as explanatory variables in route choice models. The ease and flexibility of the Python scripting language used in developing the GIS-based map-matching algorithm make this tool easy to develop and implement. It can be improved to suit data inputs and specific fields of application in transportation research. As GIS increasingly becomes a popular tool for transportation, it is timely to exploit GIS as a platform for developing postprocessing map-matching algorithms for transportation research.