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Traffic Estimation for Large Urban Road Network with High Missing Data Ratio.

Kennedy John Offor1, Lubos Vaci2, Lyudmila S Mihaylova3

  • 1Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield S1 3JD, UK. kjoffor1@sheffield.ac.uk.

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Summary

This study presents a particle-based Kriging interpolation method to handle missing traffic data in intelligent transportation systems. The approach effectively mitigates the impact of incomplete sensor data on traffic state estimation.

Keywords:
Bayesian inferenceKrigingmissing data imputationparticle filteringroad trafficstate estimation

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Area of Science:

  • Transportation Engineering
  • Data Science
  • Signal Processing

Background:

  • Intelligent transportation systems (ITS) require accurate traffic state data for network control.
  • Existing sensors often provide incomplete or missing traffic data due to various issues.
  • This data gap challenges traditional traffic estimation methods.

Purpose of the Study:

  • To develop a robust spatio-temporal traffic imputation approach for ITS.
  • To address challenges posed by high missing data rates in traffic sensor networks.
  • To evaluate the effectiveness of a novel imputation technique under varying data loss scenarios.

Main Methods:

  • Proposed a particle-based approach integrating Kriging interpolation.
  • Investigated the performance of this method across different missing data ratios.
  • Tested the approach on a large-scale road network simulation (1000 segments).

Main Results:

  • The particle-based Kriging interpolation demonstrated robustness against high missing data rates.
  • Kriging interpolation within the particle filter framework effectively mitigated the impact of missing data.
  • Performance was validated across a significant number of road network segments.

Conclusions:

  • The proposed method offers a viable solution for traffic state estimation with incomplete sensor data.
  • This approach enhances the reliability of intelligent transportation systems.
  • Effective imputation is crucial for managing and controlling complex road networks.