Abstract
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.
Highlights
The accurate prediction of future traffic conditions is crucial requirement for Intelligent Transportation Systems (ITS), which can help administrators take adequate preventive measures against congestion and travelers take better-informed decisions
To validate the efficiency of the proposed method, the performance is compared with some representative approaches, including Autoregressive Integrated Moving Average method (ARIMA) model, support vector regression (SVR), wavelet neural network (WNN), deep belief networks (DBN), and long short-term memory network (LSTM)
In SVR model, kernel function is set as Radial Basis Function (RBF), the penalty parameter of the error term as 300, and the iteration number as 1000
Summary
The accurate prediction of future traffic conditions (e.g., traffic flow, travel speed and travel time) is crucial requirement for Intelligent Transportation Systems (ITS), which can help administrators take adequate preventive measures against congestion and travelers take better-informed decisions. In order to deal with this issue, many techniques are deployed for modeling the evolution of the traffic circulation. These existing prediction schemes are classified roughly into three categories: parametric methods, nonparametric methods, and hybrid methods. The parametric methods are widely used in traffic flow prediction, but these methods are sensitive to the traffic data for different situations. The prediction accuracy of nonparametric methods and hybrid methods is superior to parametric methods, all these methods mainly considered the data closed to the prediction station, which could not fully reveal the spatiotemporal characteristics of traffic flow data. The readers interested in details of models that applied in traffic prediction field could refer to review reference paper
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
