Spatial-Temporal K Nearest Neighbors Model on MapReduce for Traffic Flow Prediction

Abstract

Researches in the area of short-term traffic flow forecasting are important for traffic flow management in intelligent transport systems. In this paper, a distributed model for short-term traffic flow prediction based on the k nearest neighbors method is presented. This model takes into account spatial and temporal traffic flow distribution. We define a feature vector for a targeted road segment using traffic flow on segments in a compact area at different time intervals. To reduce the dimensionality of the feature vector, we use principal component analysis procedure. The proposed model is based on MapReduce technology and implemented using an Apache Spark framework. An experimental study data is obtained from the transportation network of Samara, Russia.