Highway Temporal-Spatial Traffic Flow Performance Estimation by Using Gantry Toll Collection Samples: A Deep Learning Method

Abstract

In order to accurately predict the short-time traffic flow of highways, to achieve the purpose of alleviating highway traffic congestion, saving travel time, and reducing energy waste and pollution, this paper considers the spatiotemporal characteristics of the road network, uses the advantages of long short-term memory network (LSTM) to analyze time series data, divides time intervals to collect traffic flow, and substitutes it into the model. Make traffic flow predictions. Three evaluation indicators, mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE), were used for training and testing on the section k602 + 630-k636 + 090 of Chang-Jiu Expressway. It is divided into average data with intervals of 1, 3, 5, and 10 minutes for prediction. The results show that each index obtained by dividing the scale of 1 minute is the smallest, which proves that the suitable prediction scale of road traffic flow is 1 minute.