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The expected low market penetration of connected vehicles (CVs) in the nearfuture could be a constraint in estimating traffic flow parameters, such asaverage travel speed of a roadway segment and average space headway betweenvehicles from the CV broadcasted data. This estimated traffic flow parametersfrom low penetration of connected vehicles become noisy compared to 100 percentpenetration of CVs, and such noise reduces the real time prediction accuracy ofa machine learning model, such as the accuracy of long short term memory (LSTM)model in terms of predicting traffic flow parameters. The accurate predictionof the parameters is important for future traffic condition assessment. Toimprove the prediction accuracy using noisy traffic flow parameters, which isconstrained by limited CV market penetration and limited CV data, we developeda real time traffic data prediction model that combines LSTM with Kalman filterbased Rauch Tung Striebel (RTS) noise reduction model. We conducted a casestudy using the Enhanced Next Generation Simulation (NGSIM) dataset, whichcontains vehicle trajectory data for every one tenth of a second, to evaluatethe performance of this prediction model. Compared to a baseline LSTM modelperformance, for only 5 percent penetration of CVs, the analyses revealed thatcombined LSTM and RTS model reduced the mean absolute percentage error (MAPE)from 19 percent to 5 percent for speed prediction and from 27 percent to 9percent for space-headway prediction. The statistical significance test with a95 percent confidence interval confirmed no significant difference in predictedaverage speed and average space headway using this LSTM and RTS combinationwith only 5 percent CV penetration rate.

Real time Traffic Flow Parameters Prediction with Basic Safety Messages at Low Penetration of Connected Vehicles