  Missing data is a challenge in many applications, including intelligent
transportation systems (ITS). In this paper, we study traffic speed and travel
time estimations in ITS, where portions of the collected data are missing due
to sensor instability and communication errors at collection points. These
practical issues can be remediated by missing data analysis, which are mainly
categorized as either statistical or machine learning(ML)-based approaches.
Statistical methods require the prior probability distribution of the data
which is unknown in our application. Therefore, we focus on an ML-based
approach, Multi-Directional Recurrent Neural Network (M-RNN). M-RNN utilizes
both temporal and spatial characteristics of the data. We evaluate the
effectiveness of this approach on a TomTom dataset containing spatio-temporal
measurements of average vehicle speed and travel time in the Greater Toronto
Area (GTA). We evaluate the method under various conditions, where the results
demonstrate that M-RNN outperforms existing solutions,e.g., spline
interpolation and matrix completion, by up to 58% decreases in Root Mean Square
Error (RMSE).
