Highway Traffic Flow Prediction Based on Optimized KNN of Spark

Accurate prediction of high-speed traffic flow is of great significance for alleviating traffic pressure, reducing congestion, and reducing environmental pollution. Aiming at the low efficiency of the KNN prediction algorithm in the training and testing of traffic big data on a single machine, this paper proposes a distance-weighted KNN algorithm based on Spark. At the same time, cross-validation is used to tune the model K value, and the feature vector of the data is selected according to the inherent spatio-temporal correlation of the data. Experiments have proved that compared with traditional KNN, the weighted KNN model based on spatio-temporal feature relationships under Spark greatly reduces the time to find K nearest neighbors in the KNN algorithm, and the prediction efficiency is significantly improved while ensuring the prediction accuracy. This method can provide reliable support for highway management in the future.