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International Journal of Intelligent Transportation Systems Research

Erschienen in:

16.10.2024

Multiclass Traffic Assignment Model Considering Heterogeneous Stochastic Headways of Autonomous Vehicles and Human-Driven Vehicles

verfasst von: Ryuichi Tani, Teppei Kato, Kenetsu Uchida

Erschienen in: International Journal of Intelligent Transportation Systems Research | Ausgabe 3/2024

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Abstract

This study proposed a stochastic link capacity in mixed flows, considering heterogeneous stochastic headways for autonomous vehicles (AVs) and human-driven vehicles (HVs), respectively. Using this link traffic capacity, a multi-class traffic assignment problem considering mixed flows is proposed. Though previous study assumed an approximation of random variable computation to derive mixed link capacity, by assuming lognormally distributed stochastic headway, we showed the mixed link capacity model that ensure the link capacity analytically following lognormal distribution without any approximate computation of stochastic variables. Furthermore, the analytical relationship between the mean and standard deviation of the stochastic link capacity and the market penetration rate of AVs in a link is derived. We also showed the conditions for the mean and variance of the link travel time to be monotonically increasing with the link flow. The proposed traffic assignment model assumes that AVs and HVs follow UE and SUE, respectively. Numerical calculations were performed to validate the proposed model using a test network.

Vorheriger Artikel Advanced Modeling and Interpretation for Accurate Intersection Traffic Time Prediction

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Titel: Multiclass Traffic Assignment Model Considering Heterogeneous Stochastic Headways of Autonomous Vehicles and Human-Driven Vehicles
verfasst von: Ryuichi Tani
Teppei Kato
Kenetsu Uchida
Publikationsdatum: 16.10.2024
Verlag: Springer US
Erschienen in: International Journal of Intelligent Transportation Systems Research / Ausgabe 3/2024
Print ISSN: 1348-8503
Elektronische ISSN: 1868-8659
DOI: https://doi.org/10.1007/s13177-024-00430-3

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