Prediction model for expressway traffic flow of regional central cities based on time-segments

Abstract

Accurate traffic flow prediction can effectively alleviate the traffic pressure on expressways. Compared with general cities, the traffic flow on expressways in regional central cities is highly unbalanced, which makes accurate prediction of traffic flow very difficult. For this issue, we observed that traffic flow mainly varies greatly in different time periods, and the proportion of high flow time periods is relatively small. The prediction model will treat high flow data as outliers, resulting in poor prediction performance. Therefore, this paper proposes a time segment based expressway traffic flow prediction model, which maps the high and low traffic flows to different time segments. Divide the training dataset into multiple time segments, and then train the model separately based on the partitioned dataset. The experiment shows that the expressway traffic flow prediction model based on the method proposed in this paper has better performance than similar models.