Empirical Validation of Continuum Traffic Flow Model of Capacity Drop at Sag and Tunnel Bottlenecks

TL;DR

This paper empirically validates a continuum traffic flow model for capacity drops at bottlenecks like sag and tunnels, using real-world data to calibrate and assess the model's accuracy in reproducing traffic phenomena.

Contribution

It provides empirical calibration and validation of the capacity drop model at specific bottlenecks, addressing previous limitations and linking capacity changes to road gradients.

Findings

Model accurately reproduces speed recovery near queue heads.

Estimated bottleneck capacities and locations align with observed traffic conditions.

Spatial capacity changes correlate with longitudinal road gradients.

Abstract

This study validates the continuum traffic flow model of capacity drop at sag and tunnel bottlenecks, as proposed by Jin (2018) and Wada et al. (2020), through empirical analysis. Specifically, after addressing the limitations in the existing studies, we calibrate the model using data from multiple congestion events at several expressway bottlenecks. We then demonstrate that the model can reproduce the observed speed recovery near the head of the queue, and assess whether both estimated bottleneck capacities and locations are consistent with observed traffic conditions. Finally, as an application of the calibration results, we examine the relationship between the spatial changes in the estimated traffic capacity and longitudinal gradients.