An Improved K-nearest Neighbor Model for Short-term Traffic Flow Prediction

By Lun Zhang, Q. Liu, Wenchen Yang, N. Wei, Decun Dong. 2013

311 citations recorded by the source corpus.

Abstract In order to accurately predict the short-term traffic flow, this paper presents a k-nearest neighbor (KNN) model. Short-term urban expressway flow prediction system based on k-NN is established in three aspects: the historical database, the search mechanism and algorithm parameters, and the predication plan. At first, preprocess the original data and then standardized the effective data in order to avoid the magnitude difference of the sample data and improve the prediction accuracy. At last, a short-term traffic prediction based on k-NN nonparametric regression model is developed in the Matlab platform. Utilizing the Shanghai urban expressway section measured traffic flow data, the comparison of average and weighted k-NN nonparametric regression model is discussed and the reliability of the predicting result is analyzed. Results show that the accuracy of the proposed method is over 90 percent and it also rereads that the feasibility of the methods is used in short-term traffic flow prediction.

Subfields: Building and Construction, Control and Systems Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/8631046cbf350569fbe98938097a7c1c8b9dc4d3

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In Building and Construction

In Control and Systems Engineering

In Transportation

Citations in the corpus

A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges (2020, methodology): “Other types of machine learning models aside from neural network were also used, such as the kNearest Neighbor [31], [32], [33] and the Support Vector Regression [34], [35], [36].”
Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning (2019, methodology): “Statistical nonparametric algorithms applied to traffic prediction also include Bayesian models (Sun, Zhang, and Yu 2006; Zhang et al. 2017, 2018) and K-nearest neighbor (KNN) models (Zhang et al. 2013).”
A Review of Deep Learning Models for Time Series Prediction (2019, background): “The points having shortest distance with the data in training set are selected as the nearest neighbors [202]–[204].”