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.
Multiclass Traffic Assignment Model Considering Heterogeneous Stochastic Headways of Autonomous Vehicles and Human-Driven Vehicles
Int. J. ITS Res.
International Journal of Intelligent Transportation Systems Research ; 22 , 3 ; 761-773
2024-12-01
13 pages
Article (Journal)
Electronic Resource
English
Autonomous vehicle , Stochastic mixed traffic , Travel time reliability , Multiclass traffic assignment Engineering , Electrical Engineering , Automotive Engineering , Robotics and Automation , Computer Imaging, Vision, Pattern Recognition and Graphics , Civil Engineering , User Interfaces and Human Computer Interaction
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