Prediction of bus arrival time based on improved long short-term memory network model

Enhancing the precision of predicting bus arrival times can improve passenger travel efficiency and bus service quality, and save bus operating costs. Long Short Term Memory Network (LSTM) models are diffusely applied to time series analysis of bus travel time prediction tasks, but they may have shortcomings in spatial feature research. To address this issue, a prediction model mixing Convolutional Neural Network (CNN) and Long Short Term Memory Network (LSTM) comed up, which uses CNN to draw spatial features from the data, then uses LSTM networks to learn time dependencies in time series data, and introduces Attention mechanism to enhance the performance of LSTM models. Finally, the effectiveness of the prediction model is verified using bus GPS data. The test results express that the model is provided with low error.