Travel-time Prediction Using K-nearest Neighbor Method with Distance Metric of Correlation Coefficient

Real-time Travel Time (TT) information has become an essential component of daily life in modern society. With reliable TT information, road users can increase their productivity by choosing less congested routes or adjusting their trip schedules. Drivers normally prefer departure time-based TT, but most agencies in Korea still provide arrival time-based TT with probe data from Dedicated Short-Range Communications (DSRC) scanners due to a lack of robust prediction techniques. Recently, interest has focused on the conventional k-nearest neighbor (k-NN) method that uses the Euclidean distance for real-time TT prediction. However, conventional k-NN still shows some deficiencies under certain conditions. This article identifies the cases where conventional k-NN has shortcomings and proposes an improved k-NN method that employs a correlation coefficient as a measure of distance and applies a regression equation to compensate for the difference between current and historical TT. The superiority of the suggested method over conventional k-NN was verified using DSRC probe data gathered on a signalized suburban arterial in Korea, resulting in a decrease in TT prediction error of 3.7 percent points on average. Performance during transition periods where TTs are falling immediately after rising exhibited statistically significant differences by paired t-tests at a significance level of 0.05, yielding p-values of 0.03 and 0.003 for two-day data. The method presented in this study can enhance the accuracy of real-time TT information and consequently improve the productivity of road users.