A map matching algorithm based on modified hidden Markov model considering time series dependency over larger time span.

With the advancement of geopositioning systems and mobile devices, much research with geopositioning data are currently ongoing. Along with the research applications, map matching is a technology that infers the actual position of error-prone trajectory data. It is a core preprocessing technique for trajectory data. Among various map matching algorithms, map matching using Hidden Markov Model (HMM) has gained high attention. However, the HMM model simplifies the dependency of time series data excessively, which leads to inferring incorrect matching results for various situations. For example, complex road relationships or movement patterns, such as in urban areas, or serious observation errors and sampling intervals make matching more difficult. In this research, we propose a new algorithm called trendHMM map matching, which complements the assumptions of HMM. This algorithm considers a wider range of dependencies of geopositioning data by incorporating the movements of neighboring data into the matching process. For this purpose, the concept of the window containing adjacent geopositioning data is introduced. Thus trendHMM can utilize relationships among continuous geopositioning data and showed considerable enhancement over HMM-based algorithm. Through experiments, we demonstrated that trendHMM map matching provides more accurate results than the existing HMM map matching for various environments and geopositioning data sets. Our trendHMM algorithm shows up to 17.58% of performance enhancement compared to HMM based one in terms of Route Mismatch Fraction.