Research on traffic flow prediction method based on LSTM model and PSO-LSTM model

Abstract. With the acceleration of urbanization, the number of cars owned by residents has also significantly increased. The contradiction between the number of cars and the road carrying capacity has become increasingly severe, resulting in very serious congestion. This paper selects road data from 0:00 to 10:00 every morning in Beijing from April 2nd to April 12th, 2016, and uses the average speed of vehicles as a variable to measure road congestion. Based on these data, this article uses LSTM models to predict the speed of vehicles on two roads representing main and non main roads. Research has found that LSTM model has good predictive performance for the speed of vehicles on two roads and is very accurate in predicting the data trend. However, the accuracy of LSTM model in predicting non periodic and highly discrete data is not ideal. This article also uses the PSO-LSTM model to predict one of the roads, and the results show that the model is more accurate than the LSTM model in predicting non periodic data. The prediction of short-term traffic flow will greatly help the transportation department coordinate and manage resources, alleviate traffic congestion, and facilitate residents' travel.