Short-Term Traffic Flow Combined Forecasting Model Based on SVM

Yang, Yanni; Lu, Huapu

doi:10.1109/iccis.2010.70

Cited by 32 publications

(18 citation statements)

References 3 publications

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“…Analogous to the perceptron Neural Network, SVR regression process is a training process where the parameter vector of an optimal hyperplane function are determined with a set of training data by minimizing an overall error objective function. The estimated predictive function is then used for traffic variable prediction (Yang and Lu, 2010). Mathematical details of SVR are presented in section 4.3 in chapter 4.…”

Section: Support Vector Regressionmentioning

confidence: 99%

Nonlinear multivariate time–space threshold vector error correction model for short term traffic state prediction

Zhou

Abdulhai

2015

Transportation Research Part B: Methodological

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The time-space threshold vector error correction (TS-TVEC) model is proposed and developed in this research for short term (hourly) traffic state prediction. The theory and method of cointegration with error correction mechanism is employed in the general design of the new statistical model TS-TVEC. An inherent connection is revealed between the error correction model and the transformed fundamental diagrams in macroscopic traffic flow theory. Error correction model is a linear model established on difference space, whereas, the fundamental relations between traffic variables exhibit piecewise linearity when they are transformed to difference space. Meanwhile, dynamics of traffic variables can be reflected in difference space. A threshold regime switching framework is implemented to deal with unknown structural breaks in non-stationary traffic time series to capture multiple traffic states. Spatial cross-correlated information is incorporated with a piecewise linear vector error correction model. As the TS-TVEC model is designed for nonlinear multivariate forecasting, it is compared to Neural Network, and Support Vector Regression model that are commonly used in this regard to test the effectiveness and robustness of the new statistical model. The TS-TVEC model was experimented on a small scale case in our empirical study and applied on a larger scale case thereafter. Both cases show that the TS-TVEC model is an effective tool that is capable of modelling the complexity of stochastic traffic processes and potentially applicable to real time traffic state prediction.

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Section: Support Vector Regressionmentioning

confidence: 99%

Nonlinear multivariate time–space threshold vector error correction model for short term traffic state prediction

Zhou

Abdulhai

2015

Transportation Research Part B: Methodological

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“…To address the limitations of parametric models, different approaches including linear kernel, polynomial kernel, Gaussian kernel, and optimized multi kernel SVM (MK-SVM) have been proposed by recent research studies for traffic flow prediction [37][38][39][40]. MK-SVM predicted the results by Sensors 2020, 20, 685 4 of 22 mapping the linear parts of historical traffic flow data using the linear kernel, while map residual was performed using the non-linear kernel.…”

Section: Related Workmentioning

confidence: 99%

Short Term Traffic State Prediction via Hyperparameter Optimization Based Classifiers

Zahid

Chen

Jamal

et al. 2020

Sensors

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Short-term traffic state prediction has become an integral component of an advanced traveler information system (ATIS) in intelligent transportation systems (ITS). Accurate modeling and short-term traffic prediction are quite challenging due to its intricate characteristics, stochastic, and dynamic traffic processes. Existing works in this area follow different modeling approaches that are focused to fit speed, density, or the volume data. However, the accuracy of such modeling approaches has been frequently questioned, thereby traffic state prediction over the short-term from such methods inflicts an overfitting issue. We address this issue to accurately model short-term future traffic state prediction using state-of-the-art models via hyperparameter optimization. To do so, we focused on different machine learning classifiers such as local deep support vector machine (LD-SVM), decision jungles, multi-layers perceptron (MLP), and CN2 rule induction. Moreover, traffic states are evaluated using traffic attributes such as level of service (LOS) horizons and simple if–then rules at different time intervals. Our findings show that hyperparameter optimization via random sweep yielded superior results. The overall prediction performances obtained an average improvement by over 95%, such that the decision jungle and LD-SVM achieved an accuracy of 0.982 and 0.975, respectively. The experimental results show the robustness and superior performances of decision jungles (DJ) over other methods.

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“…In the field of machine learning, an SVM is a supervised learning model, usually used for pattern recognition, classification and regression analysis. In recent years, SVMs have been used in many applications such as text classification, image classification, traffic flow prediction, travel time prediction and other fields [18], [19]. Least squares SVM (LS-SVM) [20] is an extension and improvement of the SVM model.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Demand Prediction Method for Online Car-Hailing Services Based on a Least Squares Support Vector Machine

Shan

Chen

et al. 2019

IEEE Access

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The purpose of this paper is to study the short-term demand prediction for online car-hailing services problem. Prediction of short-term network car demand can provide many benefits such as an increase in the income of network car drivers. In addition, demand prediction is an important resource for recommendation systems, carpooling systems, and network car scheduling; and therefore, predicting the demand for network cars has great significance. In the last few decades, scholars have studied various problems related to short-term demand prediction for online car-hailing services based on clustering algorithms and regression algorithms. However, these studies are still problematic because the accuracy of demand prediction is not high enough. Therefore, this paper studies a method of improving the accuracy and the efficiency of demand prediction. Due to the high prediction accuracy and the fast training efficiency of least squares support vector machine (LS-SVM), a short-term demand prediction method for online car-hailing services based on LS-SVM is proposed. The modeling process involves selecting the dependent and independent variables, the basic principles of the LS-SVM, the kernel function and superparameter, model training, and prediction. In a numerical experiment, we use network car order data as the network car demand data to test the model. We show that the experimental results with the LS-SVM method implemented in this paper and then compare the model with lasso linear regression, nearest neighbor regression, decision tree regression, and neural network. The experimental results show that the short-term demand prediction model for online car-hailing services based on LS-SVM performs better than the other methods. INDEX TERMS Online car-hailing services, demand prediction, LS-SVM.

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Short-Term Traffic Flow Combined Forecasting Model Based on SVM

Cited by 32 publications

References 3 publications

Nonlinear multivariate time–space threshold vector error correction model for short term traffic state prediction

Nonlinear multivariate time–space threshold vector error correction model for short term traffic state prediction

Short Term Traffic State Prediction via Hyperparameter Optimization Based Classifiers

Short-Term Demand Prediction Method for Online Car-Hailing Services Based on a Least Squares Support Vector Machine

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