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Queue Based VDF Function

Prepared by Dr. Xuesong (Simon) Zhou’ research group at Arizona State University

Contact: [email protected]

Zhou, X.Cheng, Q., Wu, X., Li, P., Belezamo, B., Lu, J., & Abbasi, M. (2022). A meso-to-macro cross-resolution performance approach for connecting polynomial arrival queue model to volume-delay function with inflow demand-to-capacity ratio. Multimodal Transportation, 1(2), 100017.

Python implementation:

QVDF/Python_Implementation at main · asu-trans-ai-lab/QVDF (github.com)

• Cross-resolution modelling approach for understanding the dynamic relationship between demand and supply and the resulting congestion.

• Describe oversaturated system dynamics with parsimonious macroscopic analytical formulations with consistent mesoscopic queue vehicular fluid model.

• Unified integration of multi-scale models provides city planners with a valid analytical framework to analyze queue saturation evolution process.

• Estimate key model parameters from real-world data sets on heavily congested corridors.

Graphical illustration of Newell's PAQ model for a single congested period (Newell, 1982).

Major steps towards queue-oriented link performance function QVDF.

Fig. 1. Volume-speed scatters of entire year's data (from 7:00 to 21:00 ) and calibrated traffic flow model

Fig. 2 Curves between D/C ratio and average speed during congestion duration.

Fig. 3. Observed time-dependent mean speed and estimated speed at detector ID 87 on Tuesday.