Lack of Resilience in Transportation Networks: Economic Implications

TL;DR

This paper examines the economic impacts of unmitigated transportation network disruptions, emphasizing the importance of investing in resilience to prevent significant GDP losses and prolonged recovery periods.

Contribution

It introduces a model integrating transportation resilience with economic planning, demonstrating its application across multiple US cities to quantify economic impacts of disruptions.

Findings

Disruptions can cause GDP losses far exceeding initial expectations.

Resilience modeling reveals prolonged economic recovery beyond the disruption year.

Investment in resilience can significantly mitigate economic losses.

Abstract

Disruptions to transportation networks are inevitable. Currently, most mandated development-related transportation planning is intended to prepare for frequently occurring and observable disruptions while low probability events that have not yet materialized attract less attention. When road networks are not resilient, these unpredictable events can cause significant delays that may not be proportional to the extent of the disruption. Enhancing resilience can help in mitigating consequences of disruptions but requires financial investment that is difficult to justify given that low probability event may not have materialized. This paper highlights economic implications of unmitigated random disruptions in urban road systems and makes the case for investment in transportation network resilience. We utilized a model of urban transportation network performance that quantifies resilience and demonstrated its integration with microeconomic transportation planning model REMI TranSight. The model was applied to 10 cities in the USA to calculate several economic indicators under baseline scenario where economic impact was assumed to be proportional to the magnitude of disruptive events and under a test scenario where the magnitude of disruption was used to calculate additional delays in transportation networks and these additional delays were explicitly integrated in REMI model. Results show that GDP losses suffered as a result of disruption may be far more significant in the case scenario and economic output is not necessarily back to normal in the year the following disruptive event. We thus conclude that support for investment decisions on network efficiency and resilience management should be based on a framework that utilizes resilience, quantified in terms that are compatible with standard practice, and scenarios to test the implications of topological attributes.