Effect of Time Intervals on K-nearest Neighbors Model for Short-term Traffic Flow Prediction

  • Zhao Liu Southeast University
  • Xiao Qin University of Wisconsin-Milwaukee
  • Wei Huang Southeast University
  • Xuanbing Zhu Nanjing Foreign Language School
  • Yun Wei Beijing Urban Construction Design and Development Group Co. Ltd, Beijing
  • Jinde Cao Southeast University
  • Jianhua Guo Southeast University
Keywords: short-term traffic flow forecasting, point prediction, prediction interval, K-nearest neighbors, seasonal autoregressive integrated moving average (SARIMA), generalized autoregressive conditional heteroscedasticity (GARCH)

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.

Author Biographies

Zhao Liu, Southeast University
He is a PhD. student in the School of Transportation, Southeast University, China.
Xiao Qin, University of Wisconsin-Milwaukee

He is an Associate professor in the department of Civil and Environmental Engineering, University of Wisconsin-Milwaukee, Milwaukee, USA. His major research fields include intelligent transportation system applications, sustainable transportation planning, statistical methods and applications in transportation, highway safety and crash modeling. 

Wei Huang, Southeast University

He is a distinguished professor in Civil Engineer at the Intelligent Transportation System Research Center of the Southeast University. He is a member of Chinese Academy of Engineering. He enjoys the State Council special allowance and receives supports from the New Century Talent Program, the National Outstanding Mid-aged Experts Program, the National Talents Engineering Program, and the Yangtze Scholar Program from various agencies and organizations.

Xuanbing Zhu, Nanjing Foreign Language School

He is a senior student from Nanjing Foreign Language School, China.

Yun Wei, Beijing Urban Construction Design and Development Group Co. Ltd, Beijing
He is the vice director of the Urban Railway Green and Safe Construction National Engineering Laboratory. He is majoring in traffic information engineering and control and his research field includes intelligent vision analysis and pattern recognition.
Jinde Cao, Southeast University

He (M’07-SM’07-F’16) is a Distinguished Professor, the Dean of School of Mathematics and the Director of the Research Center for Complex Systems and Network Sciences at Southeast University.

Jianhua Guo, Southeast University

He is a professor in Transportation Engineering at the Intelligent Transportation System Research Center of the Southeast University. His major research fields include intelligent transportation system applications, traffic management and control, statistical time series analysis, and discrete choice modeling.

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Published
2019-03-28
How to Cite
1.
Liu Z, Qin X, Huang W, Zhu X, Wei Y, Cao J, Guo J. Effect of Time Intervals on K-nearest Neighbors Model for Short-term Traffic Flow Prediction. PROMET [Internet]. 2019Mar.28 [cited 2019Oct.18];31(2):129-3. Available from: http://traffic.fpz.hr/index.php/PROMTT/article/view/2811
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Articles