As connected and autonomous vehicle (CAV) technology continues to evolve and rapidly develop new capabilities, it is becoming increasingly important for transportation planners to consider the effects that these vehicles will have on the transportation network. It is evident that this trend has already started; over 60% of long-range transportation plans in the largest urban areas now include some discussion of CAVs, up from just 6% in 2015. There are also numerous CAV pilot programs currently underway that entail testing vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) interaction in both isolated and real-world environments. In this review of the current assessments for CAV impacts, two primary trends are identified. First, there is a great deal of uncertainty that is not being explicitly considered and properly accounted for in the transportation-network planning process. Second, the predictions that are being made are not considering potential policy or planning actions that could shape or affect the impacts of CAVs. This paper provides a picture of how ongoing CAV research interacts with current transportation planning practices by examining how the methods, the ranges of predictions, and the different sources of uncertainty in each method impact the planning process and potential system outcomes. Finally, it will identify best practices from decision analysis to help plan the best possible future transportation networks.
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Recently, efforts to model and assess a system's resilience to disruptions due to environmental and adversarial threats have increased substantially. Researchers have investigated resilience in many disciplines, including sociology, psychology, computer networks, and engineering systems, to name a few. When assessing engineering system resilience, the resilience assessment typically considers a single performance measure, a disruption, a loss of performance, the time required to recover, or a combination of these elements. We define and use a resilient engineered system definition that separates system resilience into platform and mission resilience. Most complex systems have multiple performance measures; this research proposes using multiple objective decision analysis to assess system resilience for systems with multiple performance measures using two distinct methods. The first method quantifies platform resilience and includes resilience and other "ilities" directly in the value hierarchy, while the second method quantifies mission resilience and uses the "ilities" in the calculation of the expected mission performance for every performance measure in the value hierarchy. We illustrate the mission resilience method using a transportation systems-of-systems network with varying levels of resilience due to the level of connectivity and autonomy of the vehicles and platform resilience by using a notional military example. Our analysis found that it is necessary to quantify performance in context with specific mission(s) and scenario(s) under specific threat(s) and then use modeling and simulation to help determine the resilience of a system for a given set of conditions. The example demonstrates how incorporating system mission resilience can improve performance for some performance measures while negatively affecting others.
Complex DoD systems need to be resilient due to evolving adversarial and environmental threats. The DoD created a program called Engineered Resilient Systems (ERS) to increase resilience in future systems. In our ERS research, resilience has been separated into two parts, mission and platform resilience. Mission resilience is the ability of a system to repel, resist, absorb, and recover from environments and threats that occur on planned missions. Platform resilience is the ability of a system to adapt to new missions and new threats. Using capabilities based planning one can map platform and mission resilience. This paper addresses how a value‐focused thinking (VFT) multiple objective decision analysis (MODA) model is a useful tool for capabilities based assessments. If all performance objectives can be categorized as mission or platform resilience, this extra value above the threshold of the minimum performance can provide mission and platform resilience. Displaying this information in a value component chart and a floating value chart allows decision makers to consider resilience early in the acquisition decision making process.
This paper surveys the literature on resilience, provides several definitions of resilience, and proposes a new comprehensive definition for a resilient engineered system, which is: a system that is able to successfully complete its planned mission(s) in the face of disruption(s) (environmental or adversarial), and has capabilities allowing it to successfully complete future missions with evolving threats. This definition captures the subtle differences between resilience and a resilient engineered system. We further examine the terminology associated with resilience to understand the various resilient time-frames and use the terminology to propose a resilience cycle, which differentiates mission resilience (short term) and platform resilience (long term). We then provide insight into various resilience evaluation methodologies and discuss how understanding the full scope of resilience enable designers to better incorporate resilience into system design, decision makers to consider resilient trade-offs in their assessment, and operators to better manage their systems. A resilient engineered system can lead to improved performance, reduced life-cycle costs, increased value, and extended service life for engineered systems.
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