Hybrid Multi-metric K-Nearest Neighbor Regression for Traffic Flow Prediction

Short-term prediction of traffic flow is essential for the deployment of intelligent transportation systems. In this paper we present an efficient method for short-term traffic flow prediction using a Support Vector Machine (SVM) in comparison with baseline methods, including the historical average, the Current Time Based, and the Double Exponential Smoothing predictors. To demonstrate the efficiency and accuracy of the SVM method, we used one-month time-series traffic flow data on a segment of the Pan Island Expressway in Singapore for training and testing the model. The results show that the SVM method significantly outperforms the baseline methods for most prediction intervals, and under various traffic conditions, for the rolling horizon of 30 min. In investigating the effect of the input-data dimension on prediction accuracy, we found that the rolling horizon has a clear effect on the SVM’s prediction accuracy: for the rolling horizon of 30–60 min, the longer the rolling horizon, the more accurate the SVM prediction is. To look for a solution for improvement of the SVM’s training performance, we investigate the application of k-Nearest Neighbor method for SVM training using both actual data and simulated incident data. The results show that the k- Nearest Neighbor method facilitates a substantial reduction of SVM training size to accelerate the training without compromising predictive performance.

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Spatiotemporal Traffic Flow Prediction with KNN and LSTM

Journal of Advanced Transportation ◽

10.1155/2019/4145353 ◽

2019 ◽

Vol 2019 ◽

pp. 1-10 ◽

Cited By ~ 37

Author(s):

Xianglong Luo ◽

Danyang Li ◽

Yu Yang ◽

Shengrui Zhang

Keyword(s):

Support Vector Regression ◽

Traffic Flow ◽

Prediction Models ◽

Short Term Memory ◽

Transportation Systems ◽

Support Vector ◽

K Nearest Neighbor ◽

Traffic Flow Prediction ◽

Flow Prediction ◽

Proposed Model

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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Prediction of traffic flow with small time granularity at intersection based on probabilistic network

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-179939 ◽

2020 ◽

Vol 39 (2) ◽

pp. 1659-1670

Author(s):

Wenbin Xiao ◽

Shunying Zhu ◽

Qiucheng Chen

Keyword(s):

Traffic Flow ◽

Prediction Method ◽

Small Time ◽

Traffic Density ◽

Flow Section ◽

Flow State ◽

K Nearest Neighbor ◽

Traffic Flow Prediction ◽

Probabilistic Network ◽

Flow Prediction

In order to overcome the inaccuracy of current research results of traffic flow prediction, this paper proposes a prediction method for traffic flow with small time granularity at intersection based on probability network. This method takes one minute as time granularity, collects traffic data such as cross-section flow, section traffic flow velocity data, traffic density, road occupancy, section delay and steering ratio by using RFID technology, and analyzes and processes the data. By introducing Bayesian network in probabilistic network and combining K-nearest neighbor method, historical data and predicted traffic flow state are classified to realize the prediction of traffic flow with small time granularity at intersections. The experimental results show that this method has high prediction accuracy and reliability, and is a feasible traffic flow prediction method.

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