traffic flow prediction model based on deep belief network and genetic algorithm

Zhang, Yaying; Huang, GQ

doi:10.1049/iet-its.2017.0199

Cited by 57 publications

(23 citation statements)

References 26 publications

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“…Sampling interval is 5 min and the total 288 * 15 sets of data are collected. According to the literature [35], traffic flow characteristics are different among weekday daytime, weekday nighttime, weekend daytime and weekend nighttime. Therefore, all data is divided into four categories according to the four time periods.…”

Section: Experiments and Results Analysis A Sample Collection Andmentioning

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: Experiments and Results Analysis A Sample Collection Andmentioning

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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“…Xiang et al [28], [38] developed stacked denoising auto encoder (SDAE) to predict the traffic flow with missing data. Xie et al [39] and other scholars analyzed the transport problem by a deep belief network (DBN) [40]- [43]. Sun et al [44] and other scholars applied convolutional neural network (CNN) to process time series data for traffic forecast [45]- [47].…”

Section: Literature Reviewmentioning

confidence: 99%

Traffic Flow Forecast Through Time Series Analysis Based on Deep Learning

Jiao

Huang

2020

IEEE Access

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Traffic congestion is a thorny issue to many large and medium-sized cities, posing a serious threat to sustainable urban development. Recently, intelligent traffic system (ITS) has emerged as an effective tool to mitigate urban congestion. The key to the ITS lies in the accurate forecast of traffic flow. However, the existing forecast methods of traffic flow cannot adapt to the stochasticity and sheer length of traffic flow time series. To solve the problem, this paper relies on deep learning (DL) to forecast traffic flow through time series analysis. The authors developed a traffic flow forecast model based on the long shortterm memory (LSTM) network. The proposed model was compared with two classic forecast models, namely, the autoregressive integrated moving average (ARIMA) model and the backpropagation neural network (BPNN) model, through long-term traffic flow forecast experiments, using an actual traffic flow time series from OpenITS. The experimental results show that the proposed LSTM network outperformed the classic models in prediction accuracy. Our research discloses the dynamic evolution law of traffic flow, and facilitates the decision-making of traffic management. INDEX TERMS Traffic flow forecast, time series analysis, deep learning (DL), long short-term memory (LSTM).

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“…An architecture was proposed combined with a linear model that was fitted using regularization and a sequence of tanh layers. Zhang and Huang [41] employed the genetic algorithm to find the optimal hyperparameters of DBN models. In recent years, recurrent neural network (RNN) was more practical in comparison with other deep learning structures for processing sequential data.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Traffic Flow Prediction with KNN and LSTM

Luo

Yang

et al. 2019

Journal of Advanced Transportation

206

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The traffic flow prediction is becoming increasingly crucial in Intelligent Transportation Systems. Accurate prediction result is the precondition of traffic guidance, management, and control. To improve the prediction accuracy, a spatiotemporal traffic flow prediction method is proposed combined with k-nearest neighbor (KNN) and long short-term memory network (LSTM), which is called KNN-LSTM model in this paper. KNN is used to select mostly related neighboring stations with the test station and capture spatial features of traffic flow. LSTM is utilized to mine temporal variability of traffic flow, and a two-layer LSTM network is applied to predict traffic flow respectively in selected stations. The final prediction results are obtained by result-level fusion with rank-exponent weighting method. The prediction performance is evaluated with real-time traffic flow data provided by the Transportation Research Data Lab (TDRL) at the University of Minnesota Duluth (UMD) Data Center. Experimental results indicate that the proposed model can achieve a better performance compared with well-known prediction models including autoregressive integrated moving average (ARIMA), support vector regression (SVR), wavelet neural network (WNN), deep belief networks combined with support vector regression (DBN-SVR), and LSTM models, and the proposed model can achieve on average 12.59% accuracy improvement.

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traffic flow prediction model based on deep belief network and genetic algorithm

Cited by 57 publications

References 26 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

Traffic Flow Forecast Through Time Series Analysis Based on Deep Learning

Spatiotemporal Traffic Flow Prediction with KNN and LSTM

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