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

Yu Han, Pan Liu, Zhiyuan Liu, Ludovic Leclercq

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.

Capacity drop accounting for microscopic vehicle interaction effects: analytical model and validation with high-resolution 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.
Paper Structure (17 sections, 28 equations, 18 figures, 2 tables)

This paper contains 17 sections, 28 equations, 18 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Literature review
  3. Observations of capacity drop
  4. Mechanisms on capacity drop
  5. The impact of acceleration delay on QDFs
  6. Description of data
  7. Driver's acceleration delay and its impact on QDF
  8. The impact of wave-void interaction on QDF
  9. Illustration of wave-void interaction
  10. Analytical model to estimate QDF
  11. Model validation
  12. QDF comparison between standing queues and jam waves
  13. Validation through Numerical Simulations
  14. Sensitivity analysis
  15. Validation using real data
  16. ...and 2 more sections

Figures (18)

  • Figure 1: The geographical and topological map of the sites for data collection.
  • Figure 2: (a) Color-coded vehicle trajectories of the innermost lane of site 1, and (b) The rescaled cumulative curves.
  • Figure 3: (a) Color-coded vehicle trajectories of the innermost lane of site 2, and (b) The rescaled cumulative curve.
  • Figure 4: An example of a platoon where hesitant vehicles created voids.
  • Figure 5: Acceleration response delays of vehicle 1 (a) and 2 (b) during deceleration-acceleration processes.
  • ...and 13 more figures