Capacity drop accounting for microscopic vehicle interaction effects: analytical model and validation with high-resolution trajectories

TL;DR

This paper introduces an analytical model for capacity drop in traffic, attributing it to hesitant vehicles and their interactions, validated with simulations and real-world data.

Contribution

The paper presents a novel analytical approach to estimate capacity drop considering microscopic vehicle interactions and void formation.

Findings

The model accurately predicts capacity drop phenomena.

Interactions between downstream and upstream hesitant vehicles are key.

Validation confirms the model's effectiveness with real trajectories.

Abstract

Capacity drop is a traffic phenomenon in which the discharge flow from a queue is lower than the theoretical infrastructure capacity. This paper proposes a generic analytical method to estimate the queue discharge flow of freeway traffic. Capacity drop is primarily attributed to hesitant vehicles, defined as vehicles that stochastically and temporarily enter an acceleration delay state and generate voids (i.e., extra gaps) in front of them. The proposed method estimates the expected total void length generated by all hesitant vehicles, based on the distributions of their spatial and temporal locations as well as the associated delays. It also accounts for interactions between the waves triggered by downstream hesitant vehicles and the voids generated by upstream ones. Our analysis reveals that this interaction is the key mechanism behind the differing extents of capacity drop observed between standing queues and jam waves in previous studies. The accuracy of the model is validated through both numerical simulations and real-world trajectories. Overall, the proposed method offers a deeper understanding of capacity drop, which can be leveraged in traffic flow modeling and control.