Refinement of Pattern-Matching Method for Travel Time Prediction
This paper reconsiders how to define the similarity between historical and current day travel time data in pattern matching for travel time prediction. The core idea of pattern matching is designing a measurement of similarity, and the similarity function is intuitively presumed to have a negative exponential distribution. Here, a gamma distribution that includes this exponential distribution is introduced as an alternative. Complimentary mechanisms are also tried. The results of application to data from an urban expressway are summarized as follows: although the prediction accuracy of the refined method is only slightly better than pattern matching based on previous pattern matching, marginally better fitness is found.
travel time prediction pattern matching data-driven approach