Abstract

Although attention to data fusion has undergone rapid growth since the late 1980s, there are still relatively few applications in transportation management. Most research has based fusion weight estimation on the variance of each data source, assigning high weights to low variance data, implying that low variance means high accuracy. We propose a data fusion methodology where weights are assigned in a way data variance and sensor bias are minimized, but also consistency among data sources is maximized. The proposed methodology is flexible to work with multiple data sources, with different reliability and variability, and under different traffic conditions. The inclusion of consistency is shown to be statistically significant during special events and incidents and demonstrates its validity in successfully representing changes in traffic patterns by reasonably estimating their magnitude. Results from a case study that validate this method are shown.