Development of a Transportation Network Analysis Algorithm to Assess System Resilience
Session Number
J02
Advisor(s)
Megan Clifford, Argonne National Laboratory Casey Trail, Argonne National Laboratory Thomas Wall, Argonne National Laboratory
Location
A-131
Start Date
28-4-2016 8:00 AM
End Date
28-4-2016 8:25 AM
Abstract
Roads accommodate a large portion of the movement of people and goods across transportation systems, but are vulnerable to man-made and natural disasters. To minimize the impacts of potential disruptions on the road network’s users, network resilience, the ability to resist, react, and adapt to disruptions must be improved. Investing into the network is critical to improving resilience, but requires a systematic analysis, such as computerized network analysis, to identify and prioritize the most critical infrastructure system components for investment. In this study, we develop a network routing approach that uses the shortest path algorithm to connect randomly selected origin points to a destination and ranks sections of the paths by usage level, which correlates to potential impact on network resilience. We then apply this method to a transportation system case study in Ogden, Utah, and show that the method can prioritize critical transportation network components. This method helps identify critical infrastructure in a time-efficient manner, and can be systematically applied to networks that vary in size and complexity. By identifying critical transportation network infrastructure, experts can receive information to be used for improving system resilience in planning for the future.
Development of a Transportation Network Analysis Algorithm to Assess System Resilience
A-131
Roads accommodate a large portion of the movement of people and goods across transportation systems, but are vulnerable to man-made and natural disasters. To minimize the impacts of potential disruptions on the road network’s users, network resilience, the ability to resist, react, and adapt to disruptions must be improved. Investing into the network is critical to improving resilience, but requires a systematic analysis, such as computerized network analysis, to identify and prioritize the most critical infrastructure system components for investment. In this study, we develop a network routing approach that uses the shortest path algorithm to connect randomly selected origin points to a destination and ranks sections of the paths by usage level, which correlates to potential impact on network resilience. We then apply this method to a transportation system case study in Ogden, Utah, and show that the method can prioritize critical transportation network components. This method helps identify critical infrastructure in a time-efficient manner, and can be systematically applied to networks that vary in size and complexity. By identifying critical transportation network infrastructure, experts can receive information to be used for improving system resilience in planning for the future.