Short-term traffic flow velocity prediction model based on multi-channel data fusion of meteorological and traffic

In order to improve the prediction accuracy of short-term traffic flow under the influence of meteorological and traffic factors, a short-time traffic flow velocity prediction model based on Gramian angular field-convolutional neural network-long short-term memory (GAF-CNN-LSTM) was proposed by considering the fusion of meteorological and traffic feature data such as temperature, humidity, and traffic congestion index. By utilizing the Gramian angular field, time series data of temperature, humidity, and historical traffic flow velocity were transformed into image data. RGB multi-channel color coding was employed to generate meteorological and traffic feature images, and the color superposition changes in the RGB muti-channel reflected the meteorological and traffic multi-factor feature data fusion. The transformed feature images were input into a convolutional neural network to extract fusion features of meteorological and traffic factors. An long short-term memory neural network was employed to capture the time series information of meteorological and traffic factors, thereby a short-term traffic flow velocity prediction model was constructed. Meteorological data, including temperature, humidity, and historical traffic flow velocity data from the Weiyang District in Xi'an were selected. According to the changing trend of temperature and humidity, two extreme scenarios of minimum humidity and minimum temperature with maximum humidity, and two conventional scenarios of non-extreme temperature and humidity were set to verify the model. Analysis results indicate that the GAF-CNN-LSTM model can consider the fusion of meteorological and traffic multi-factor feature data. Compared with moving average model, autoregressive integrated moving average model, prophet model, random forest model, and LSTM neural network model, the mean square error, root mean square error, and mean absolute percentage error of GAF-CNN-LSTM model reduce by 0.044 6, 0.142 4, and 12.1%, respectively, while the determination coefficient shows an average improvement of 31.05%. The GAF-CNN-LSTM model exhibits the highest prediction accuracy, and the results can provide a more accurate decision-making basis for urban transportation governance.