Short-Term Traffic Volume Forecast Method Based On CNN-LSTM-At

Abstract

In order to tackle existing traffic flow prediction problem, a Traffic Volume Forecast Model based on deep learning is designed. The model implements Convolutional Neural Network (CNN) to extract spatial matrix information, uses long and short-term neural network (LSTM) for sequence prediction, appends attention mechanism to time step on LSTM, and assigns weights to different time steps. By implementing model verification on the Chengdu taxi dataset, dividing data into various categories, cross validating different categories of data, and comparing the model with other models, it is concluded that the CNN-LSTM-At network model proposed in this article has higher accuracy compared with traditional network model.

Short-term urban road congestion prediction considering temporal-spatial correlation

“…In order to improve the prediction accuracy of the model, this paper uses the PCC algorithm for spatial feature mining. From the evaluation results, it can be seen that: when the time step is small (1,5,6), as the value of the spatial threshold value decreases, the accuracy continues to decrease and RMSE continues to increase, adding the PCC algorithm at this time instead reduces the prediction accuracy; when the time step increases to a certain number (7,8,9,10). As the value of the spatial threshold is taken to decrease, the accuracy first increases to reach a peak and then gradually decreases, and RMSE first decreases and then gradually increases.…”

“…Step 4 Markov property test: use equations ( 4), ( 5), ( 6) and (7) to test whether the time series has Markov property.…”

“…At present, Markov chains are more often used in the field of traffic flow prediction in the direction of transportation. Chen Shuyan et al proposed a prediction method combining wavelet analysis and Markov magic, which improved the anti-interference of the prediction model [4]; Jiang Lizhong et al proposed to combine Grey model and Markov chain model to form Grey Markov chain model for traffic flow prediction, and the experiment showed that the model has certain feasibility [5]; Li Junhuai et al proposed to combine exponential smoothing theory and Markov chain, and the experiment showed that the combined model is more accurate than the single model [6]; Xu Mengmeng et al improved the prediction accuracy of the model by correcting the residuals on the basis of the Grey Markov model [7]; Shen Qidong et al introduced traffic flow prediction influence factors from time and space to achieve traffic flow prediction of intersections [8].…”