Research on Prediction of Urban Road Congestion Based on Spark-GBDT

Abstract

The problem of urban road congestion is the key to be solved urgently in China's urban traffic. To effectively predict it, this paper proposes a method for predicting urban road congestion based on the Spark platform parallel Gradient Boosting Decision Tree algorithm. First, the basic principle of GBDT algorithm is briefly introduced. Secondly, the GBDT algorithm based on the parallel design of the Spark big data platform is used to predict urban road congestion. Finally, through accuracy experiments and scalability experiments, the effectiveness of the algorithm and the performance of the algorithm under different numbers of nodes are verified in the Spark cluster. Experiments prove that the method proposed in this paper can effectively predict urban road congestion, reduce the running time, improve the prediction efficiency, and provide effective help for urban road management.