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Group-Based Dimensionality Reduction and Estimation for Heterogeneous Large-Scale Traffic Networks
- Publication Year :
- 2022
- Publisher :
- Zenodo, 2022.
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Abstract
- State estimation for traffic networks is a particu-larly challenging problem in view of their large dimensionality, and since models are often inaccurate and the interaction pat-terns unpredictable. In this article, we approach the problem by mixing aggregation-based complexity reduction and nonlinear filtering. We subdivide vehicles into groups and derive a lower-dimensional approximate model where vehicles belonging to the same group are represented by a unique random variable matching their average characteristics. Then, we propose a procedure to estimate the statistical properties of the group variables from partial measurements. Connections to car-following models are discussed, and the developed methodology is illustrated through numerical simulations.
- Subjects :
- Large-scale systems, road traffic, state estimation
Subjects
Details
- Database :
- OpenAIRE
- Accession number :
- edsair.od......2659..ae917ba41ecf32c5243691b6a4f17060