Empirical Investigation of Fundamental Diagrams in Mixed Traffic
نویسندگان
چکیده
A thorough understanding of the fundamental relation traffic flow variables is critical for efficient operation systems. However, their relationships in mixed are challenging to model due continuously changing vehicle composition. This paper proposes a composition-based approach estimating between using empirical data. The methodology seeks eliminate difficulties class-specific ss identification by introducing continuous wavelet transformation with oblique cumulative arrival and occupancy time plots. We used machine learning (ML) algorithms delineate regimes showed diagrams given location that has composition-invariant free-flow branch but distinct composition-specific branches congestion. Also, it was observed congested regime (CR) wide scatter indicating possible stochastic inter-class interactions varying vehicular proposed distance optimization method re-cluster CR data found improves fit observations. result illustrates heavy vehicles will dominate high-speed increase AO. It beyond level AO congestion, all class travel at same speed. Finally, validation different datasets shows robust under conditions.
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2023
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2023.3242971