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Missing Traffic Data Imputation with a Linear Generative Model Based on Probabilistic Principal Component Analysis.

Liping Huang1, Zhenghuan Li1, Ruikang Luo1

  • 1School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore.

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Summary
This summary is machine-generated.

This study introduces a new model for imputing missing traffic speed data that is missing not at random (MNAR). The approach effectively recovers data on different road links, outperforming existing methods.

Keywords:
missing dataprincipal component analysisprobabilisticurban traffic sensing

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Area of Science:

  • Intelligent Transportation Systems
  • Data Science
  • Traffic Engineering

Background:

  • Ubiquitous sensing data in intelligent transportation systems (ITS) faces challenges with missing traffic data due to detector faults or limited probe vehicles.
  • Existing traffic data imputation methods primarily address data missing at random (MAR) and overlook link-specific missing data distinctions.
  • Traffic speed data in real-world scenarios are often missing not at random (MNAR), necessitating specialized imputation techniques.

Purpose of the Study:

  • To propose a novel model for imputing MNAR traffic speed data in ITS.
  • To introduce a metric for distinguishing road links based on missing data characteristics.
  • To evaluate the model's performance in recovering missing data across links with varying characteristics.

Main Methods:

  • Development of a general linear model based on probabilistic principal component analysis (PPCA) for MNAR traffic speed imputation.
  • Introduction of the Pearson score (p-score) metric to differentiate road links based on missing data patterns.
  • Comparative analysis of the proposed model against the standard PPCA model using the p-score metric.

Main Results:

  • The proposed PPCA-based linear model demonstrates superior performance in imputing MNAR traffic speed data compared to the standard PPCA model.
  • The study successfully utilizes the p-score to distinguish road links with different missing data properties.
  • Missing data on links with higher p-score values are shown to be more effectively recovered by the new model.

Conclusions:

  • The developed model offers an effective solution for MNAR traffic speed data imputation in ITS.
  • The p-score metric provides valuable insights into link-specific data missingness, aiding in targeted imputation.
  • This research advances traffic estimation by improving data completeness and accuracy, particularly in challenging MNAR scenarios.