Deep Convolutional Mesh Rnn For Urban Traffic Passenger Flows Prediction

Urban traffic passenger flows prediction is practically important to facilitate many real applications including transportation management and public safety. Sustained and rapid economic growth requires an orderly organization, and planning is an indispensable part of an orderly organization process. The reduction in travel efficiency due to traffic congestion, as well as energy and various pollution issues from the transportation sector, have become the bottleneck for the further development of the city and are the most troublesome topic for governments in all countries. Recently, deep learning performs the excellent ability to extract high dimensional spatial-temporal characters in regression and classification tasks. In this paper, we propose a deep learning model based on CNN and RNN, which takes matrixed traffic as input, uses CNN to extract traffic characteristics, and uses RNN to predict the evolution of features to achieve traffic flow prediction. Instead of traditional rnn models, we design a new type of RNN structure unit that can process time data in multiple time dimensions at the same time. Using a network-like RNN model, the evolution of traffic flow in different time dimensions is fully considered, and the interaction between different time dimensions is taken into account to predict the traffic flow of the target time series. The prediction of each data in the sequence has real data as input instead of merely taking the output of the previous moment as the input of the next moment. Experiments show that our model can significantly improve the prediction accuracy for real traffic passenger flow datasets.