Bus passenger flow classification prediction driven by CNN-GRU model and multi-source data

To accurately analyze the trip characteristics and time-varying differences of different passenger flows of bus routes and stops, combined with deep learning theory, a bus passenger flow classification prediction model based on a combination of a convolutional neural network (CNN) and gated recurrent unit (GRU) was proposed. By integrating and matching multi-source data, such as bus card swiping, bus global positioning system (GPS) trajectory, route and station basic information, and weather data, bus passenger flow data was reconstructed. The K-medians algorithm was used to divide passengers into commuter and non-commuter categories. Taking the factors of passenger type, historical passenger flow, time period, high/flat peak, week, precipitation, and major events as input vectors, a single model of CNN and GRU was established, and forecasts were conducted using mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) as evaluation indicators. As a single model is not suitable for multi-feature time series forecasting, line passenger flow and cross-section passenger flow prediction models combined with a CNN and GRU were constructed. Taking Beijing Special 15 Bus as an example, the classified passenger flows of routes and cross-sections under the scenarios of working days and non-working days were predicted. Analysis results show that for commuter and non-commuter routes and cross-section passenger flows, the MSEs of the combined model reduce by 57.932, 13.106, and 33.987 on average, the RMSEs reduce by 1.862, 1.058, and 1.538 on average, and the MAEs reduce by 1.399, 0.487, and 0.613 on average, respectively. Thus, the CNN-GRU combined model driven by multi-source data has a good prediction performance. 3 tabs, 7 figs, 36 refs.