Evaluating the impacts of major transportation disruptions - San Francisco Bay Area case study

The constraints of transportation networks are fundamental to disaster planning. Having the capability of evaluating the emergent dynamics of such networks in the context of large traffic incidents can inform the design of traffic management strategies. On February 7, 2019, the Richmond-San Rafael Bridge in the San Francisco Bay Area, connecting multiple cities and carrying over 100 000 vehicles daily, had to be suddenly closed for over 9 hours due to a structural failure of its upper deck. This incident caused major disruptions in the region as the typical traffic was interrupted and detoured as travelers found alternate routes. In this study, we demonstrate the capability of large-scale traffic impact assessments of major network disruptions using the Richmond-San Rafael Bridge closure as a case study. Using a high-performance, parallel-discrete event traffic simulation, we assess the traffic impacts resulting from the bridge closure at both the regional system and city levels. Our model estimates that the region incurred an additional 14 000 vehicle hours of delay and 600 000 vehicle miles in distance due to the bridge closure. The incident affected over 55 000 trips; certain trips experienced an increase of 46 min in delay and 26 miles in travel distance. The median traffic volume on neighborhood streets in San Francisco, Vallejo, and San Rafael increased by 30%, 22%, and 13%, respectively. The results suggest that the cities' local roads provided the additional adaptive capacity to disperse the traffic. With large-scale modeling of a critical network disruption using dynamic rerouting capability, complete road network, and full demand, we provide valuable insights into the response dynamics of this specific event. In doing so, the value of such regional analyses to incident and disaster planning is demonstrated. (c) 2024 Tongji University and Tongji University Press. Publishing Services by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY- NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).