A novel learning method for multi-intersections aware traffic flow forecasting

Recent advances in machine learning have helped solve many challenges in artificial intelligence applications, such as traffic flow forecasting. Traffic flow forecasting models based on machine learning have recently been widely applied because of their great generalisation capability. This study aims to construct a multi-intersection-aware traffic flow prognostication architecture considering recent information of a nearby road, which is a significant indicator of the near-future traffic flow, and considering the selection of appropriate and essential sensors significantly correlated to the future traffic flow. To capture the inner correlation between sequential traffic flow data, a novel learning method involving the relevance vector machine is employed for the traffic flow forecasting. To optimise the kernel parameters of the relevance vector machine, a combination of the chaos theory and a simulated annealing algorithm is adopted. The proposed model is verified with the real-world data of six roads in a Minnesotan city. Then, the forecasting results of the new model are compared with those of some state-of-the-art models. These results indicate that the application of relevance vector regression to short-term traffic flow forecasting combined with a chaos-simulated annealing algorithm to optimise the corresponding parameters is a high-precision and -scalability short-term traffic flow forecasting method. The multi-intersection-aware mechanism helps improve the forecasting accuracy.