Reusability of the Output of Map-Matching Algorithms Across Space and Time Through Machine Learning

A map-matching algorithm outputs a vector per GPS point, projecting the moving object on one of the segments of the transportation network. Although developing more sophisticated map-matching algorithms for vehicle and pedestrian navigation systems have been the focus of research in this field, reusability of the historical information already provided by map-matching algorithms has not been addressed yet. In other words, although researchers have been attempting to improve the accuracy of the aforementioned vector to correctly project GPS points on the transportation network, no research has exploited the spatial-temporal pattern in the arrangement of these projection vectors. This pattern, if properly detected, can be used as a rough surrogate for map-matching algorithms, in addition to other applications that require better positional accuracy for moving objects in smart cities. This paper detects and validates the spatial-temporal pattern in projection vectors produced by map-matching algorithms via machine learning. Projection vectors showed a strong spatial-temporal pattern in Chicago, IL, USA, which was captured best via a local nonlinear regressor, K-nearest neighbors, and helped double the positional accuracy of unseen GPS points. While a global nonlinear regressor, multilayer Perceptron was able to slightly improve the positional accuracy of GPS points, the linear least squares had an exacerbating effect on the positional accuracy.