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Using Tensor Completion Method to Achieving Better Coverage of Traffic State Estimation from Sparse Floating Car Data
Bin Ran1, Li Song2, Jian Zhang1
1Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Jiangsu Province Collaborative Innovation Center for Technology and Application of Internet of Things, Room 213, Transportation Building, Southeast University, No. 2 Si Pai Lou, Nanjing, 210096, P.R. China.
Plos One
|July 23, 2016
Summary
Traffic state estimation using floating car data is improved by a novel tensor completion method. This approach effectively imputes missing traffic data, even with very low floating car penetration rates below 1%.
Area of Science:
- Transportation Engineering
- Data Science
- Network Analysis
Background:
- Traffic state estimation from floating car systems faces challenges due to low data penetration and sparse spatial-temporal coverage.
- Existing methods struggle to provide comprehensive traffic state information for entire road networks.
Purpose of the Study:
- To develop a robust traffic state estimation method for road networks using floating car data.
- To address the limitations of current approaches in estimating traffic states for uncovered road segments.
Main Methods:
- The study frames traffic state estimation as a missing data imputation problem.
- A tensor completion framework is proposed to model and estimate missing traffic states, utilizing spatial and temporal correlations.
Main Results:
- The proposed tensor completion approach effectively estimates traffic states from sparse floating car data.
- Reliable estimations were achieved even with floating car penetration rates below 1% in simulation networks.
Conclusions:
- Tensor completion offers a powerful framework for comprehensive traffic state estimation from limited floating car data.
- The method successfully leverages multi-dimensional correlations within traffic data for improved accuracy.
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