How did the Urban Network Flow Adapt to the Collapse of the Carola Bridge?

TL;DR

This study analyzes how urban traffic in Dresden adapted to the unexpected collapse of the Carola Bridge, revealing significant rerouting, increased bridge usage, prolonged peak hours, and shifts to Park-and-Ride modes, informing resilience strategies.

Contribution

Develops a data-driven framework to assess short-term traffic adaptation following infrastructure disruption using traffic data and statistical analysis.

Findings

Albert bridge traffic increased by up to 81%.

Peak hours extended up to 250 minutes.

Park-and-Ride usage increased by up to 188%.

Abstract

The unexpected collapse of the Carola Bridge in Dresden, Germany, provides a rare opportunity to characterise how urban network traffic adapts to an unexpected infrastructure disruption. This study develops a data-driven analytical framework using traffic data from the Dresden traffic management system to assess the short-term impacts of the disruption. By combining statistical comparisons of pre- and post-collapse motorised traffic distributions, peak-hour shifts, and Park-and-Ride data analyses, the framework reveals how traffic dynamics and traveller choices adjust under infrastructure disruption. Results reveal that the two closest bridges, the Albert and Marien Bridges, absorb the majority of the diverted motorised traffic. In particular, the daily traffic volume on the Albert bridge increases by up to 81%, which is equivalent to 3.5 hours of traffic operating with maximum flow. Peak hours on critical links are significantly prolonged, reaching up to 250 minutes. Besides redistribution, the overall daily motorised traffic crossing the Elbe river declines by approximately 8,000 vehicles, while Park-and-Ride usage increases by up to 188%, suggesting a potential travel mode shift after the disruption. The study reveals the patterns of traffic redistribution following an unexpected disruption and provides insights for resilience planning and emergency traffic management.