Estimation of Missing Data in Intelligent Transportation System

TL;DR

This paper presents an ML-based Multi-Directional Recurrent Neural Network approach for estimating missing traffic data in ITS, effectively leveraging spatio-temporal features to outperform existing methods.

Contribution

The paper introduces a novel M-RNN model that captures both temporal and spatial data characteristics for missing data estimation in ITS.

Findings

M-RNN reduces RMSE by up to 58% compared to existing methods.

The approach effectively handles missing data in real-world traffic datasets.

M-RNN outperforms spline interpolation and matrix completion techniques.

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 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).