LAUSR

We present the SnapNet system, which provides accurate real-time map matching for cellular-based trajectory traces. Such traces are characterized by input locations that are far from the actual road segment, errors on the order of kilometers, back-and-forth transitions, and highly sparse input data. SnapNet applies a series of filters to handle the noisy locations and an interpolation stage to address the data sparseness. At the core of SnapNet is a novel incremental HMM algorithm that combines digital map hints in the estimation process and a number of heuristics to reduce the noise and provide real-time estimations. Evaluation of SnapNet using actual traces from different cities covering more than 400 km shows that it can achieve a precision and recall of more than 90% under noisy coarse-grained input location estimates. This maps to over 97% and 34% enhancement in precision and recall, respectively, when compared to the traditional HMM map-matching algorithms. Moreover, SnapNet has a latency of 0.58 ms per location estimate.