A resilience-oriented approach for quantitatively assessing recurrent spatial-temporal congestion on urban roads

Area of Science:

• Transportation Engineering
• Urban Planning
• Resilience Science

Background:

• Traffic congestion causes delays, environmental concerns (greenhouse gases, air pollutants), and road safety risks.
• Existing methods for characterizing urban congestion (time-based, level of service) lack a systemic perspective.
• Resilience, the ability of a system to cope with and recover from disturbances, offers a novel framework for analysis.

Purpose of the Study:

• To propose a modified metric, inspired by the R4 resilience framework, for quantifying recurrent urban traffic congestion.
• To construct a metric using dimensions from resilience engineering and transport science.
• To evaluate the proposed metric's effectiveness by comparing it with existing approaches using spatial-temporal traffic patterns.

Main Methods:

• Developed a novel metric by adapting the R4 resilience-triangle framework to model recurrent congestion as an internal disturbance.
• Integrated dimensions from resilience engineering and transport science into the metric.
• Compared the proposed metric's performance against two other methods using data from freeway and signal-controlled arterial road cases.

Main Results:

• The proposed metric effectively captures congestion patterns and provides a quantitative benchmark for comparison.
• It demonstrates strong comparative performance and accounts for the traffic discharging process during congestion.
• Sensitivity tests indicate robustness against parameter perturbations but highlight the influence of ϵ on pattern identification.

Conclusions:

• The developed metric offers a systemic and alternative approach to conventional congestion assessment.
• It enriches the available tools for evaluating traffic congestion by incorporating resilience concepts.
• Future research will focus on large-scale validation across diverse traffic conditions and scenarios.