On the Impact of Floating Car Data and Data Fusion on the Prediction of the Traffic Density, Flow and Speed Using an Error Recurrent Convolutional Neural Network

Traffic prediction helps mitigate the impact of traffic congestion. The accuracy of traffic predictions depends on the availability of the data used for the prediction as well as the prediction model. Data from fixed traffic detectors is only available at certain locations. On the other hand, connected vehicles can provide Floating Car Data (FCD) at any location and time. However, FCD may not be available at all vehicles, and this can impact predictions since the FCD may not reflect the state of all traffic. This impact is larger when predicting traffic density or flow, and existing studies generally use FCD to predict traffic speed or travel time only. This study proposes a traffic prediction model that can accurately predict the three fundamental traffic variables (traffic density, flow, and speed) using FCD and an error recurrent convolutional neural network that takes as input the three variables. These are estimated using FCD and data from induction loops. These estimates depend on the penetration rate of FCD, so we propose a method to locally and dynamically estimate this penetration rate. This method improves the estimation of the traffic variables, and hence their prediction. The proposed model is used to analyze the impact of the FCD penetration rate on the prediction of the traffic variables. We show how our proposal reduces the amount of FCD needed to improve the prediction obtained with data from traffic detectors. We show that our proposal only requires FCD from 4% of the vehicles to improve the prediction accuracy achieved with traffic detectors. Augmenting this percentage increases the accuracy of our model for the three traffic variables. However, we also show that our prediction model reduces the FCD sample size (or FCD penetration rate) needed to achieve prediction accuracy levels close to that obtained if all vehicles provided FCD.