@article{Wen2023, 
author = {Wen Wen and Ying Liu and Rui-chu Cai and Zhi-feng Hao},
title = {Spatial-temporal Deep Regression Model for Multi-granularity Traffic Flow Prediction},
year = {2023},
journal = {Journal of Guangdong University of Technology},
volume = {40},
number = {4},
pages = {1-8},
keywords = {multi-granularity, spatio-temporal correlation, taffic flow, deep learning, autoregressive},
url = {https://www.sciopen.com/article/10.12052/gdutxb.220157},
doi = {10.12052/gdutxb.220157},
abstract = {Traffic flow prediction is an important problem in the field of intelligent transportation systems. Most existing traffic-flow prediction methods have made good progress, which however still face the following two key challenges. (1) The underlying pattern of traffic flow depends on not only the historical information along the timeline, but also the information of spatially adjacent areas, making it a challenging problem on how to balance the two temporal-spatial patterns; (2) Due to fact that time information has the characteristic of multiple granularity, such as hour, day and week, how to capture the multi-grained temporal patterns is another challenge problem. In this paper, we design a multi-grained spatio-temporal deep regression model  (MGSTDR) to address the above challenges. By extending the typical autoregressive integrated moving average model (ARIMA) on the basis of multi-grained spatio-temporal traffic flow information, the proposed model can effectively use historical information along the timeline as well as the information of adjacent regions, such that the prediction of multi-grained spatio-temporal traffic flow can be performed. Experimental results on several datasets demonstrate that the proposed model outperforms existing benchmark methods on the task of multi-granularity, and particularly obtains an approximately 5.66% improvement in the hourly traffic flow prediction.}
}