Modeling stochastic human-driver car following behavior in oscillatory traffic conditions


Raphael Stern, Mingfeng Shang

August 2021

Report no. CTS 21-06

Accurately modeling the realistic and unstable traffic dynamics of human-driven traffic flow is crucial to being able to understand how traffic dynamics evolve, and how new agents such as autonomous vehicles might influence traffic flow stability. This work is motivated by a recent dataset that allows us to calibrate accurate models, specifically in conditions when traffic waves arise. Three microscopic car-following models are calibrated using a microscopic vehicle trajectory dataset that is collected with the intent of capturing oscillatory driving conditions. For each model, five traffic flow metrics are constructed to compare the flow-level characteristics of the simulated traffic with experimental data.

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