A Predicting Method of Urban Traffic Network Volume Based on STARIMA Model
Deyong Guan, Lei Huang, Qiankun Qu · CICTP 2017 · 2018
This paper presents a short term regional traffic flow forecasting model based spatial-temporal dependency. A tree structure was abstracted representing the relations of downstream and upstream links according to the topological relations of a regional urban road network. The multiple distribution of turning rates at the intersections on the route from upstream to downstream was used to quantify the spatial-temporal dependency with the quantified spatial-temporal dependency and then used to modify the spatial weight matrix of the STARIMA (space-time autoregressive integrated moving average) model. The parameters of the STARIMA model were calibrated using the historical traffic flow data and exploited to the short term traffic flow forecasting. The experimental results show that the improved STARIMA model can provide better forecasting performance as a new approach for short term traffic flow forecasting of a regional road network.