Seq2Img-DRNET: A travel time index prediction algorithm for complex road network at regional level

Nowadays, traffic situation prediction is a major concern. Travel time index prediction is one of the representative tasks in traffic situation prediction. Most research of the current prediction focuses on road -level speed, but the travel time index prediction of complex road network at the regional level has not been well solved. In this paper, we proposed a novel model with convolutional neural network called Seq2Img-DRNET. The sequence data with 10 min interval are converted into image data as the input of our model. The model is constructed by fusing dense connection network and residual network to solve the travel time index prediction of complex road network at the regional level. Taking two actual traffic networks of commercial district and scenic district in Chengdu as the example, we compared our model with common sequential model and convolution neural network model, and the results show that our model can effectively capture the spatial-temporal relationship characteristics of complex road network at the regional level and make accurate prediction.

Automated summary

This paper introduces a novel model based on convolutional neural network, which converts sequence data into image data and combines dense connection network and residual network to effectively solve the travel time index prediction problem of complex road networks at the regional level.