Interpreting the Wide Scattering of Synchronized Traffic Data by Time Gap Statistics

TL;DR

This paper offers a statistical interpretation of the scattered flow-density data in synchronized traffic by analyzing time gap variations and their impact on traffic flow models, revealing different scaling laws in free and congested conditions.

Contribution

It introduces a quantitative approach linking time gap variability to traffic flow scattering, accounting for inhomogeneity and correlations in congested traffic.

Findings

Flow-density data align with jam line when considering propagation speed variations.

Most probable time gaps increase upstream of bottlenecks in congestion.

Different power-law scaling laws for time gap variance in free and congested traffic.

Abstract

Based on the statistical evaluation of experimental single-vehicle data, we propose a quantitative interpretation of the erratic scattering of flow-density data in synchronized traffic flows. A correlation analysis suggests that the dynamical flow-density data are well compatible with the so-called jam line characterizing fully developed traffic jams, if one takes into account the variation of their propagation speed due to the large variation of the netto time gaps (the inhomogeneity of traffic flow). The form of the time gap distribution depends not only on the density, but also on the measurement cross section: The most probable netto time gap in congested traffic flow upstream of a bottleneck is significantly increased compared to uncongested freeway sections. Moreover, we identify different power-law scaling laws for the relative variance of netto time gaps as a function of the sampling size. While the exponent is -1 in free traffic corresponding to statistically independent time gaps, the exponent is about -2/3 in congested traffic flow because of correlations between queued vehicles.