Short‐term traffic forecasting using self‐adjusting k‐nearest neighbours

Sun, Bin; Cheng, Wei; Goswami, Prashant; Bai, Guohua

doi:10.1049/iet-its.2016.0263

Cited by 77 publications

(45 citation statements)

References 46 publications

(57 reference statements)

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“…The parametric approach includes the historical average (HA) method, autoregressive moving average method (ARIMA) [6,7], seasonal autoregressive integrated moving average method (SARIMA) [8][9][10], and Kalman filter (KF) [11,12]. The nonparametric approach includes artificial neural networks (ANNS) [13][14][15][16][17], k-nearest neighbor (KNN) [18][19][20][21][22], support vector regression (SVR) [23,24], and the Bayesian model [25,26]. The hybrid approach mainly combines the parametric approach with the nonparametric approach [27][28][29][30][31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Traffic Flow Prediction during the Holidays Based on DFT and SVR

Luo

Zhang

2019

Journal of Sensors

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With the implementation of the freeway free policy during the holidays, traffic congestion in the freeway becomes a common phenomenon. In order to alleviate traffic pressure, traffic flow prediction during the holidays has become a problem of great concern. This paper proposes a hybrid prediction methodology combining discrete Fourier transform (DFT) with support vector regression (SVR). The common trend in the traffic flow data is extracted using DFT by setting an appropriate threshold, which is predicted by extreme extrapolation of the historical trend. The SVR method is applied to predict the residual series. The experimental results with measured data collected from the toll stations in Jiangsu province of China show that the proposed algorithm has higher accuracy compared with the traditional method, and it is an efficient method for traffic flow prediction during the holidays.

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Section: Introductionmentioning

confidence: 99%

Traffic Flow Prediction during the Holidays Based on DFT and SVR

Luo

Zhang

2019

Journal of Sensors

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“…Most of cases researchers have implemented KNN method for prediction of financial products. The main motto of this algorithm is to classify the objects using majority voting procedure among neighbour data points and took the class which is getting major votes among all [23][24][25][26].…”

Section: B K-nearest Neighbors Algorithmmentioning

confidence: 99%

Forecasting Foreign Currency Exchange Price using Long Short-Term Memory with K-Nearest Neighbor Method

Nayak,

Pavitra,

Tripathy

et al. 2019

IJEAT

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With the growing population in the world, economic stability varies day by day. In case of India all banking transaction rules and regulations are taken by Reserve bank of India (RBI) whereas for other countries it is different. Therefore numerous academicians have projected their research on forecasting the currency exchange rate for diverse countryside. Foreign currency exchange rate prediction is a very pivotal task for international market. Hence researchers have explored different methods for predicting foreign currency exchange rate. In this work, we have taken Indian rupees (INR) with two different country’s data set such as Japanese yen (JPY) andChinese Yuan (CNY)for daily, weekly and monthlyprediction beforehand. We implemented a hybrid model oflong short-term memory (LSTM) with K-nearest neighbour (KNN) which gives better opening price prediction accuracy on our dataset. The accuracy of the prediction results are measured by the help of performance standards such as mean absolute percentage error (MAPE) and root mean square error (RMSE).

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“…The parametric methods are widely used in traffic flow prediction, but these methods are sensitive to the traffic data for different situations. The nonparametric methods include artificial neural networks (ANNS) [6][7][8][9], k-nearest neighbor (KNN) [10][11][12][13][14], support vector regression (SVR) [15,16], and Bayesian model [17,18]. Compared to the parametric methods, nonparametric methods are more effective in prediction performance.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Traffic Flow Prediction with KNN and LSTM

Luo

Yang

et al. 2019

Journal of Advanced Transportation

177

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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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Short‐term traffic forecasting using self‐adjusting k‐nearest neighbours

Cited by 77 publications

References 46 publications

Traffic Flow Prediction during the Holidays Based on DFT and SVR

Traffic Flow Prediction during the Holidays Based on DFT and SVR

Forecasting Foreign Currency Exchange Price using Long Short-Term Memory with K-Nearest Neighbor Method

Spatiotemporal Traffic Flow Prediction with KNN and LSTM

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