A Method of Speed Data Fusion Based on Bayesian Combination Algorithm and Markov Model

The variety of data collecting and communication methods used in intelligent transportation systems such as sensors, cameras, and communication networks bring about huge volumes of data that are available for numerous transportation applications and related research on smart cities. However, it is still a challenge to integrate these heterogeneous data sources into a singular data schema. Compared to a single data source, higher data accuracy can be expected through the integration of multiple data sources if the data quality from each source is known. In this study, a data fusion method based on Bayesian fusion rules is proposed to merge traffic speed from different data sources according to their prior probability that can be inferred from a high-order, multivariable Markov model that is itself developed in a systemic perspective. Finally, case studies based on freeway data, such as integrated freeway loop data, INRIX data, and data from the National Performance Management and Research Data Set (NPMRDS), are performed in order to validate the effectiveness of proposed speed fusion method