The Lifecycle Benefit–Cost Analysis for a Rural Bridge Construction to Support Energy Transportation

Abstract

Abstract:Rural roads represent an important part of the economy in the Great Plains and Midwest of the United States. As oil exploration in North Dakota increases, the number of vehicles travelling on these roads is also increasing, resulting in significant needs for infrastructure maintenance and upgrades, including the construction of new bridges. The lifecycle benefit-cost analysis of bridge construction is the overall objective of this research. This paper investigates the benefits of combining travel demand modeling and lifecycle benefit-cost analysis, and demonstrates traffic forecasting using geographic information systems (GIS) and network flow problem. The paper also shows a comparison of the alternative scenarios. The authors demonstrate a method of integrating geographic information systems, operations research, and data analytics to estimate future traffic by evaluating infrastructure needs. The travel demand modeling is essential for the lifecycle benefit-cost analysis. This study would help government agencies and construction companies evaluate and prioritize new bridge construction projects. The agencies should consider adding the environmental costs.

An integrated geospatial approach for repurposing wind turbine blades

“…3.6.1 GIS and information models and database integration -The last decade has seen an increasing awareness of the benefits and need to integrate nonspatial information databases and models within a geographically located framework. Examples include combining system dynamic models for sustainable urban development (Xu and Coors, 2012); hydropower energy storage (Fitzgerald, 2012); energy and life cycle decision support (Ferreira et al, 2013;Mutel et al, 2012); and cost-benefit analysis for energy transportation (Dharmadhikari et al, 2016). Fig.…”

Location Allocation of Sugar Beet Piling Centers Using GIS and Optimization

“…The GIS software uses a shortest path algorithm to create the O-D matrix. This method is similar to the method in Dharmadhikari, Lee, and Kayabas [17]. The O-D matrix can be generated in two ways-1) distance in miles or 2) travel time between O-D pair.…”

“…This helps to provide a combined network to use in the GIS analysis. The process followed in this step is similar to the process in Dharmadhikari, Lee, and Kayabas [17]. Sugar beet truck fuel efficiency is assumed to be 10 miles per gallon and average fuel cost is assumed to be $3.00 per gallon for the study period.…”