Real-time Traffic Data Prediction with Basic Safety Messages using Kalman-Filter based Noise Reduction Model and Long Short-term Memory Neural Network
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With the development of Connected Vehicle (CV) technology, temporal variation of roadway traffic can be captured by sharing Basic Safety Messages (BSMs) from each vehicle using the communication between vehicles as well as with transportation roadside infrastructures (e.g., traffic signal) and traffic management centers. However, the penetration of connected vehicles in the near future will be limited. BSMs from limited CVs could provide an inaccurate estimation of current speed or space headway. This inaccuracy in the estimated current average speed and average space headway data is termed as noise. This noise in the traffic data significantly reduces the prediction accuracy of a machine learning model, such as the accuracy of long short term memory (LSTM) model in predicting traffic condition. To improve the real time prediction accuracy with low penetration of CVs, we developed a traffic data prediction model that combines the LSTM with a noise reduction model (the standard Kalman filter or Kalman filter based Rauch Tung Striebel (RTS)). The average speed and space headway used in this study were generated from the Enhanced Next Generation Simulation (NGSIM) dataset, which contains vehicle trajectory data for every one tenth of a second. Compared to a baseline LSTM model without any noise reduction, for 5 percent penetration of CVs, the analyses revealed that combined LSTM model reduced the mean absolute percentage error (MAPE) from 19 percent to 5 percent for speed prediction and from 27 percent to 9 percent for space headway prediction. The overall reduction of MAPE value ranged from 1 percent to 14 percent for speed and 2 percent to 18 percent for space headway prediction compared to the baseline model.
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