Use of Local Linear Regression Model for Short-Term Traffic Forecasting

2003 ◽  
Vol 1836 (1) ◽  
pp. 143-150 ◽  
Author(s):  
Hongyu Sun ◽  
Henry X. Liu ◽  
Heng Xiao ◽  
Rachel R. He ◽  
Bin Ran
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Nearest Neighbor ◽  
Time Of Day ◽  
Local Linear Regression ◽  
K Nearest Neighbor ◽  
Short Term ◽  
Traffic Forecasting ◽  
Local Linear

The traffic-forecasting model, when considered as a system with inputs of historical and current data and outputs of future data, behaves in a nonlinear fashion and varies with time of day. Traffic data are found to change abruptly during the transition times of entering and leaving peak periods. Accurate and real-time models are needed to approximate the nonlinear time-variant functions between system inputs and outputs from a continuous stream of training data. A proposed local linear regression model was applied to short-term traffic prediction. The performance of the model was compared with previous results of nonparametric approaches that are based on local constant regression, such as the k-nearest neighbor and kernel methods, by using 32-day traffic-speed data collected on US-290, in Houston, Texas, at 5-min intervals. It was found that the local linear methods consistently showed better performance than the k-nearest neighbor and kernel smoothing methods.

scholarly journals A Hybrid Short-term Traffic Flow Forecasting Method Based on Neural Networks Combined with K-Nearest Neighbor

2018 ◽  
Vol 30 (4) ◽  
pp. 445-456 ◽  
Author(s):  
Zhao Liu ◽  
Jianhua Guo ◽  
Jinde Cao ◽  
Yun Wei ◽  
Wei Huang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Traffic Flow ◽  
Nearest Neighbor ◽  
Network Models ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Short Term ◽  
Neural Network Models ◽  
Traffic Forecasting

It is critical to implement accurate short-term traffic forecasting in traffic management and control applications. This paper proposes a hybrid forecasting method based on neural networks combined with the K-nearest neighbor (K-NN) method for short-term traffic flow forecasting. The procedure of training a neural network model using existing traffic input-output data, i.e., training data, is indispensable for fine-tuning the prediction model. Based on this point, the K-NN method was employed to reconstruct the training data for neural network models while considering the similarity of traffic flow patterns. This was done through collecting the specific state vectors that were closest to the current state vectors from the historical database to enhance the relationship between the inputs and outputs for the neural network models. In this study, we selected four different neural network models, i.e., back-propagation (BP) neural network, radial basis function (RBF) neural network, generalized regression (GR) neural network, and Elman neural network, all of which have been widely applied for short-term traffic forecasting. Using real world traffic data, the  experimental results primarily show that the BP and GR neural networks combined with the K-NN method have better prediction performance, and both are sensitive to the size of the training data. Secondly, the forecast accuracies of the RBF and Elman neural networks combined with the K-NN method both remain fairly stable with the increasing size of the training data. In summary, the proposed hybrid forecasting  approach outperforms the conventional forecasting models, facilitating the implementation of short-term  traffic forecasting in traffic management and control applications.

scholarly journals Short Term Load Forecasting for Electric Power Utilities: A Generalized Regression Approach Using Polynomials and Cross-Terms

2021 ◽  
Vol 12 (1) ◽  
pp. 21
Author(s):  
Aneeque Ahmed Mir ◽  
Kafait Ullah ◽  
Zafar A. Khan ◽  
Furrukh Bashir ◽  
Tauseef Ur Rehman Khan ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Predictive Models ◽  
Load Forecasting ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Cross Terms ◽  
Generalized Regression

With the emergence of advanced computational technologies, the capacity to process data for developing machine learning-based predictive models has increased multifold. However, reliance on the model’s mere accuracy has swiftly shifted attention away from its interpretability. Resultantly, a need has emerged amongst forecasters and academics to have predictive models that are not only accurate but also interpretable as well. Therefore, to facilitate energy forecasters, this paper advances the knowledge of short-term load forecasting through generalized regression analysis using high degree polynomials and cross terms. To predict the irregularly changing energy demand at the consumer level, the proposed model uses a time series of an hourly load of three years of an electricity distribution company in Pakistan. Two variants of regression analysis are used: (a) generalized linear regression model (GLRM), and (b) generalized linear regression model with polynomials and cross-terms (GLRM-PCT) for comparative reasons. Experiments revealed that GLRM-PCT showed higher forecasting accuracy across a variety of performance metrics such as mean absolute percentage error (MAPE), mean absolute error (MAE), root mean squared error (RMSE), and r-squared values. Moreover, the enhanced interpretability of GLRM-PCT also explained a wide range of combinations of weather variables, public holidays, as well as lagged load and climatic variables.

scholarly journals A Sample-Rebalanced Outlier-Rejected $k$ -Nearest Neighbor Regression Model for Short-Term Traffic Flow Forecasting

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 22686-22696 ◽  
Author(s):  
Lingru Cai ◽  
Yidan Yu ◽  
Shuangyi Zhang ◽  
Youyi Song ◽  
Zhi Xiong ◽  
...  
Keyword(s):  
Regression Model ◽  
Traffic Flow ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Short Term ◽  
Traffic Flow Forecasting
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