Method of Predicting Bus Arrival Time Based on MapReduce Combining Clustering with Neural Network

Bus arrival time prediction is the basis of promoting the development of urban public transport services. In order to solve the prediction problem, a model of predicting bus arrival time based on MapReduce combining clustering with neural network was proposed. Firstly, according to the running characteristics of the bus, the running time of the bus is divided by the K-means clustering method, and the bus running data in the same period has high similarity. Secondly, the BP neural network model is established respectively to forecast the arriving time. Thirdly, a parallel prediction model based on MapReduce is established on the Hadoop platform for the segmentation prediction model which is the combination of clustering and neural network. Finally, the actual bus running data is used for the simulation and verification. Experimental results show that the new prediction model is better than the traditional BP neural network prediction model and has higher prediction accuracy and prediction speed.