United States Department of Transportation
i
Informing Post-Disaster Restoration through Modeling Interdependent Agriculture and Transportation Networks [supporting datasets]
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2021-12-01
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[PDF-174.36 KB]
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Abstract:The U.S. multimodal transportation network is extensively used to transport agricultural inputs and outputs. Disruptions to the transportation network can therefore cause severe and cascading operational and economic damage to the food and agriculture sector. These disruptions are magnified due to the food and agriculture sector's interdependency with other critical infrastructure sectors, dependence on time-sensitive operations, and positioning within rural communities. This research investigates (i) how multimodal transportation should be used when a disruption has occurred and (ii) how to coordinate restoration activities across interdependent transportation and agriculture systems. We present a mixed integer linear programming model that seeks to maximize the expected yield for a set of rice farms throughout the state of Arkansas under scenarios where the amount and timeliness of fertilizer delivery are affected by a disrupted transportation network. We validate the model using real data for a case study in Arkansas. This dataset includes the location and acreage of rice farms in Arkansas, fertilizer demand, and a multimodal transportation network comprised of road, rail, and waterway networks. For this dataset, we created disruption scenarios for different transportation modes with different severity, location, and duration. Using these data, we ran extensive computational experiments to deduce operational and restoration insights on the interdependence and resiliency of transportation and agriculture systems. The findings demonstrate the need for coordination, systems thinking, and understanding of how transportation impacts other interdependent infrastructures. Additionally, the model may help to establish a methodology for planning in the agricultural sector.
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