Prediction of Bus Arrival Time Based on Gated Recurrent Unit Neural Networks - LU Juntian;SUN Ling;SHI Quan (Nantong Daxue xuebao. Ziran kexue ban)
In order to increase the public transportation usage and the reasonability of the bus schedule by the management department, a novel prediction model of bus arrival time is proposed. This predicting model based on gated recurrent unit(GRU) neural network, analyzed the big data of historical GPS data about floating vehicle and considers the influence of different routes, bus station location, different drivers, weather conditions, time distribution and other factors. Furthermore, combining more than 50 million pieces of raw data, the model uses Spark elastic distributed data set in distributed Hadoop cluster to clean data and site matching algorithm to match source data, Lasso algorithm to optimize feature options and remove interference. The simulation results reveal that the R-square fitting degree of the improved GRU model is 94.547% and the prediction efficiency is nearly 14% higher than that of traditional long short-term(LSTM) model. It provides a reference for further improving the accuracy and efficiency of bus arrival time prediction.