  Large-scale geolocation telematics data acquired from connected vehicles has
the potential to significantly enhance mobility infrastructures and operational
systems within smart cities. To effectively utilize this data, it is essential
to accurately match the geolocation data to the road segments. However, this
matching is often not trivial due to the low sampling rate and errors
exacerbated by multipath effects in urban environments. Traditionally,
statistical modeling techniques such as Hidden-Markov models incorporating
domain knowledge into the matching process have been extensively used for
map-matching tasks. However, rule-based map-matching tasks are noise-sensitive
and inefficient in processing large-scale trajectory data. Deep learning
techniques directly learn the relationship between observed data and road
networks from the data, often without the need for hand-crafted rules or domain
knowledge. This renders them an efficient approach for map-matching large-scale
datasets and makes them more robust to the noise. This paper introduces a
sequence-to-sequence deep-learning model, specifically the transformer-based
encoder-decoder model, to perform as a surrogate for map-matching algorithms.
The encoder-decoder architecture initially encodes the series of noisy GPS
points into a representation that automatically captures autoregressive
behavior and spatial correlations between GPS points. Subsequently, the decoder
associates data points with the road network features and thus transforms these
representations into a sequence of road segments. The model is trained and
evaluated using GPS traces collected in Manhattan, New York. Achieving an
accuracy of 76%, transformer-based encoder-decoder models extensively employed
in natural language processing presented a promising performance for
translating noisy GPS data to the navigated routes in urban road networks.
