Traffic flow prediction based on depthwise separable convolution fusion network

Traffic flow prediction is an important part of an intelligent transportation system to alleviate congestion. In practice, most small and medium-sized activities are not given priority in transport planning, yet these activities often bring about a surge in demand for public transport. It is recognized that such patterns are inevitably more difficult to predict than those associated with day-to-day mobility, and that forecasting models built using traffic data alone are not comprehensive enough. Aiming at this problem, a depthwise separable convolutional fusion forecast network (FFN) was proposed by focusing on the impact of event information on traffic flow demand. FFN fused heterogeneous data to model traffic data, weather information, and event information extracted from the Internet. The depthwise separable one-dimensional convolution was used to encode the textual information describing the event layer by layer, and local one-dimensional sequence segments (ie subsequences) were extracted from the sequence to retain rich local semantic features. In the modeling process, the interaction of heterogeneous data was established, that is, the temporal and other data were used to drive the textual information representation in the encoding process to capture better relevant textual representations. Finally, information from different sources and formats was fused to obtain a joint feature representation tensor that predicts the traffic demand in the next day's event area. The experimental results show that the average absolute error of the fusion prediction network is reduced by 26.5%, the root mean square error is reduced by 11.6%, and the judgment coefficient is increased by 26.4% compared with the prediction network that only considers the traffic data.