K-nearest Neighbor Non-parametric Regression Research Articles

Determination of Reactivity Ratios from Binary Copolymerization Using the k-Nearest Neighbor Non-Parametric Regression.

This paper proposes a new method for calculating the monomer reactivity ratios for binary copolymerization based on the terminal model. The original optimization method involves a numerical integration algorithm and an optimization algorithm based on k-nearest neighbour non-parametric regression. The calculation method has been tested on simulated and experimental data sets, at low (<10%), medium (10–35%) and high conversions (>40%), yielding reactivity ratios in a good agreement with the usual methods such as intersection, Fineman–Ross, reverse Fineman–Ross, Kelen–Tüdös, extended Kelen–Tüdös and the error in variable method. The experimental data sets used in this comparative analysis are copolymerization of 2-(N-phthalimido) ethyl acrylate with 1-vinyl-2-pyrolidone for low conversion, copolymerization of isoprene with glycidyl methacrylate for medium conversion and copolymerization of N-isopropylacrylamide with N,N-dimethylacrylamide for high conversion. Also, the possibility to estimate experimental errors from a single experimental data set formed by n experimental data is shown.

Short-term Traffic Flow Prediction based on Data-Driven Knearest neighbour Nonparametric Regression

In order to solve the problem that the KNN algorithm is not accurate enough in the short-term traffic forecasting process and the prediction efficiency is not high caused by the search for past observations, a short-term traffic flow prediction model based on data-driven K-nearest neighbor non-parametric regression is proposed. The model is developed based on two-step search algorithm. Firstly, in the non-predictive period of time, the candidate input data similar to the current state is searched from the historical data for the system. Then, the optimal decision input data for prediction is identified from the candidate input data at the predictive point. Finally, the best decision input data is used to generate the prediction through the prediction algorithm. The simulation results show that the algorithm can effectively reduce the time for searching historical data on the premise of ensuring the accuracy of system prediction, thus reducing the execution time in the process of system prediction and improving the prediction efficiency of the system.

An Improved Double-layer K-nearest Neighbor Nonparametric Regression Method for Short-time Traffic Flow Prediction

In combination with the repeatability of the traffic flow state patterns, this article improved the k-nearest neighbor non-parametric regression method. To be specific, the neighbors were screened twice and the function based on state pattern recognition was introduced; moreover, the traffic flows in the past time and the traffic flows towards the related directions at both upstream and downstream crossroads were taken into account, so that the predictive ability of the proposed k-nearest neighbor non-parametric regression method can be improved. In addition, the final prediction results were output using the weighted average method of the reciprocal of the state pattern vector matching distance, so as to enhance the accuracy and real-time performance of the short-term traffic flow prediction.

A Short-Term Traffic Flow Forecasting Method Based on a Three-Layer K-Nearest Neighbor Non-Parametric Regression Algorithm

Short-term traffic flow is one of the core technologies to realize traffic flow guidance. In this article, in view of the characteristics that the traffic flow changes repeatedly, a short-term traffic flow forecasting method based on a three-layer K-nearest neighbor non-parametric regression algorithm is proposed. Specifically, two screening layers based on shape similarity were introduced in K-nearest neighbor non-parametric regression method, and the forecasting results were output using the weighted averaging on the reciprocal values of the shape similarity distances and the most-similar-point distance adjustment method. According to the experimental results, the proposed algorithm has improved the predictive ability of the traditional K-nearest neighbor non-parametric regression method, and greatly enhanced the accuracy and real-time performance of short-term traffic flow forecasting.

Potentialities of Data-Driven Nonparametric Regression in Urban Signalized Traffic Flow Forecasting

Single-interval forecasting of traffic variables plays a key role in modern intelligent transportation systems (ITSs). Despite the achievements of advanced ITS forecasting in literature, forecast modeling of urban signalized traffic flow, which shows rapid-intensive fluctuations associated with the nonlinear and nonstationary behavior of temporal evolution, is still one of its big challenges. From the perspective of field experts, the mathematical complexity of an advanced model is also a renewal obstacle in practice. On the other hand, the accessibility of large volumes of historical data and the concurrent advanced data management systems used to access them provide data-driven nonparametric regression with a renewal opportunity in practice. In order to address these problems effectively, this paper proposes a k nearest neighbor nonparametric regression (KNN-NPR) forecasting methodology to be tested against vast quantities of real traffic volume data collected from urban signalized arterials. The results show that the KNN-NPR model is clearly superior to two parametric models, Kalman filtering and seasonal autoregressive integrated moving average (ARIMA), in terms of both prediction accuracy and the construction of the directionality of temporal state evolution without a time-delayed response. Consequently, it appears that KNN-NPR, even though it is very simplified, is able to efficaciously capture the complex behavior of urban signalized traffic flow.