Abstract
The accuracy and reliability in predicting short-term traffic flow is important. The K-nearest neighbors (K-NN) approach has been widely used as a nonparametric model for traffic flow prediction. However, the reliability of the K-NN model results is unknown and the uncertainty of traffic flow point prediction needs to be quantified. To this end, we extended the K-NN approach by constructing the prediction interval associated with the point prediction. Recognizing the stochastic nature of traffic, time interval used to measure traffic flow rate is remarkably influential. In this paper, extensive tests have also been conducted after aggregating real traffic flow data into time intervals, ranging from 3 minutes to 30 minutes. The results show that the performance of traffic flow prediction can be improved when the time interval increases. More importantly, when the time interval is shorter than 10 minutes, K-NN can generate higher accuracy of the point prediction than the selected benchmark model. This finding suggests the K-NN model may be more appropriate for traffic flow point and interval prediction at a shorter time interval.
Highlights
The performance of intelligent transportation systems (ITS) largely depends on the accuracy and reliability of the real-time traffic information
This study extends the K-nearest neighbors (K-NN) model to construct the prediction interval at the 95% confidence level
As a nonparametric regression model, the K-NN model is compared to the benchmark seasonal autoregressive integrated moving average (SARIMA) + generalized autoregressive conditional heteroscedasticity (GARCH) model
Summary
The performance of intelligent transportation systems (ITS) largely depends on the accuracy and reliability of the real-time traffic information. Short-term traffic flow prediction is fundamental for improving urban transportation systems operations, management and safety. Many forecasting models have been developed and applied for predicting short-term traffic flow. Conditions, driver behavior and transportation policies constantly challenges the development of tangible and technically sound traffic flow forecasting models. The traditional short-term traffic flow forecasting aims to predict an average value according to historical traffic patterns and trends. Traffic flow interval prediction has been considered a key performance measure of transportation systems and methodologies have been proposed to quantify the reliability of the point prediction [3,4,5,6,7]
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