Flow-aware WPT k-nearest neighbours regression for short-term traffic prediction

Robust and accurate traffic prediction is critical in modern intelligent transportation systems (ITS). One widely used method for short-term traffic prediction is k-nearest neighbours (kNN). However, choosing the right parameter values for kNN is problematic. Although many studies have investigated this problem, they did not consider all parameters of kNN at the same time. This paper aims to improve kNN prediction accuracy by tuning all parameters simultaneously concerning dynamic traffic characteristics. We propose weighted parameter tuples (WPT) to calculate weighted average dynamically according to flow rate. Comprehensive experiments are conducted on one-year real-world data. The results show that flow-aware WPT kNN performs better than manually tuned kNN as well as benchmark methods such as extreme gradient boosting (XGB) and seasonal autoregressive integrated moving average (SARIMA). Thus, it is recommended to use dynamic parameters regarding traffic flow and to consider all parameters at the same time.