Multiple sources and multiple measures based traffic flow prediction using the chaos theory and support vector regression method

Cheng, Anyu; Xiao, Jiang; Li, Yongfu; Zhang, Chao; Zhu, Hao

doi:10.1016/j.physa.2016.09.041

Cited by 108 publications

(34 citation statements)

References 33 publications

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“…Simultaneously, the prediction accuracy and robustness of models are improved obviously. There are some representative methods in this field such as support vector regression (SVR) [5]- [7], K-nearest neighbour (KNN) [8], artificial neural network (ANN) [9], [10]. Zhang et al put forward a hybrid forecasting framework based on an improved SVR for traffic flow prediction.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Optimization Long Short-Term Memory Model Based on Data Preprocessing for Short-Term Traffic Flow Prediction

Zhang

Xin

2020

IEEE Access

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In order to eliminate outliers in traffic flow data collection and promote the generalization performance of traffic flow prediction, this paper proposes a dynamic optimization long short-term memory (LSTM) model based on data preprocessing for short-term traffic flow prediction. A new classification algorithm named Asym-Gentle Adaboost with Cost-sensitive support vector machine (AGACS) is used for preprocessing traffic flow data. AGACS tries to employ Cost-sensitive SVM (CS-SVM) as weak component classifier in Asymmetric Gentle AdaBoost, and divide the data collection into outlier data and normal data. Only normal data is used for training LSTM to predict traffic flow and an improved chaotic Particle Swarm Optimization (CPSO) is used for dynamic optimizing hidden layer structure of LSTM to promote the generalization and robustness performance of model. The efficiency of the proposed method is proved with real traffic flow data, and the experimental results show that preprocess collecting data and dynamic optimize model structure are conducive to improve the performance of algorithm, and the proposed method achieved better performance than those of three other classical deep learning prediction methods.

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Section: Introductionmentioning

confidence: 99%

Dynamic Optimization Long Short-Term Memory Model Based on Data Preprocessing for Short-Term Traffic Flow Prediction

Zhang

Xin

2020

IEEE Access

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“…Hong [31] and Duan [32], respectively applied support vector machine models based on simulated annealing algorithm and particle swarm optimization to predict the traffic flow. Cheng et al [33] combined the chaos theory with support vector regression model for the traffic flow prediction. Tang et al [34] combined the data de-noising method and support vector machine for traffic flow prediction.…”

Section: Introductionmentioning

confidence: 99%

Short-term Traffic Flow Prediction Based on Genetic Artificial Neural Network and Exponential Smoothing

Tan

2020

PROMET

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In order to improve the accuracy of short-term traffic flow prediction, a combined model composed of artificial neural network optimized by using Genetic Algorithm (GA) and Exponential Smoothing (ES) has been proposed. By using the metaheuristic optimal search ability of GA, the connection weight and threshold of the feedforward neural network trained by a backpropagation algorithm are optimized to avoid the feedforward neural network falling into local optimum, and the prediction model of Genetic Artificial Neural Network (GANN) is established. An ES prediction model is presented then. In order to take the advantages of the two models, the combined model is composed of a weighted average, while the weight of the combined model is determined according to the prediction mean square error of the single model. The road traffic flow data of Xuancheng, Anhui Province with an observation interval of 5 min are used for experimental verification. Additionally, the feedforward neural network model, GANN model, ES model and combined model are compared and analysed, respectively. The results show that the prediction accuracy of the optimized feedforward neural network is much higher than that before the optimization. The prediction accuracy of the combined model is higher than that of the two single models, which verifies the feasibility and effectiveness of the combined model.

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“…To date, many forecasting methods have been used to predict resource problems. These methods are divided into three categories: short-term traffic flow prediction techniques based on vector data flow (such as the wavelet analysis [12], support vector machine [13], the chaotic prediction model [14,15], and neural network [16,17]), short-term traffic flow prediction techniques based on matrix data flow [18][19][20][21] (such as the multivariate time series prediction model [18] and the Kalman filtering method [20]), and short-term traffic flow prediction techniques based on tensor data flow (such as the seasonal autoregressive integrated moving average + generalized autoregressive conditional heteroscedasticity (SARIMA + GARCH) model [22] and seasonal selfvector regression (Seasonal-SVR) prediction model [23]). The abovementioned prediction models are usually based on a large sample size and thus cannot be used to solve smallscale problems.…”

Section: Introductionmentioning

confidence: 99%

A Multimode Dynamic Short‐Term Traffic Flow Grey Prediction Model of High‐Dimension Tensors

Duan

Xiao

2019

Complexity

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Short-term traffic flow prediction is an important theoretical basis for intelligent transportation systems, and traffic flow data contain abundant multimode features and exhibit characteristic spatiotemporal correlations and dynamics. To predict the traffic flow state, it is necessary to design a model that can adapt to changing traffic flow characteristics. Thus, a dynamic tensor rolling nonhomogeneous discrete grey model (DTRNDGM) is proposed. This model achieves rolling prediction by introducing a cycle truncation accumulated generating operation; furthermore, the proposed model is unbiased, and it can perfectly fit nonhomogeneous exponential sequences. In addition, based on the multimode characteristics of traffic flow data tensors and the relationship between the cycle truncation accumulated generating operation and matrix perturbation to determine the cycle of dynamic prediction, the proposed model compensates for the periodic verification of the RSDGM and SGM grey prediction models. Finally, traffic flow data from the main route of Shaoshan Road, Changsha, Hunan, China, are used as an example. The experimental results show that the simulation and prediction results of DTRNDGM are good.

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Multiple sources and multiple measures based traffic flow prediction using the chaos theory and support vector regression method

Cited by 108 publications

References 33 publications

Dynamic Optimization Long Short-Term Memory Model Based on Data Preprocessing for Short-Term Traffic Flow Prediction

Dynamic Optimization Long Short-Term Memory Model Based on Data Preprocessing for Short-Term Traffic Flow Prediction

Short-term Traffic Flow Prediction Based on Genetic Artificial Neural Network and Exponential Smoothing

A Multimode Dynamic Short‐Term Traffic Flow Grey Prediction Model of High‐Dimension Tensors

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