Empirical Features of Congested Traffic States and Their Implications for Traffic Modeling

TL;DR

This study analyzes real-world congested traffic patterns on a German highway, identifying five types of congestion and examining their features, which informs and compares with existing traffic flow theories and models.

Contribution

The paper provides empirical characterization of diverse congestion patterns and discusses their implications for traffic modeling, especially contrasting first- and second-order macroscopic models.

Findings

Identified five distinct congestion patterns and their combinations.

Documented the 'boomerang effect' as a sign of linearly unstable traffic flow.

Compared empirical data with theoretical traffic models.

Abstract

We investigate characteristic properties of the congested traffic states on a 30 km long stretch of the German freeway A5 north of Frankfurt/Main. Among the approximately 245 breakdowns of traffic flow in 165 days, we have identified five different kinds of spatio-temporal congestion patterns and their combinations. Based on an "adaptive smoothing method" for the visualization of detector data, we also discuss particular features of breakdowns such as the "boomerang effect" which is a sign of linearly unstable traffic flow. Controversial issues such as "synchronized flow" or stop-and-go waves are addressed as well. Finally, our empirical results are compared with different theoretical concepts and interpretations of congestion patterns, in particular first- and second-order macroscopic traffic models.