Estimation of Missing Data in Intelligent Transportation System

Abstract

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 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 priori 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).

A new low complexity bus travel time estimator for fleet management system

“…This may overshadow the estimated values and reduce the performance quality of the estimation algorithm. In [30] the authors used machine learning to estimate the lost data. This estimated data, along with other data, completes the data set to make the bus arrival time estimation algorithm more efficient.…”