| Networked infrastructures serve as essential backbones of our society. Examples of such critical infrastructures whose destruction severely impacts the defense or economic security of our society include transportation, telecommunications, power grids, and water supply networks. Among them, road transportation networks have a principal role in people's everyday lives since they facilitate physical connectivity. The performance of a road transportation network is governed by the three principal components: (a) structure, (b) dynamics, and (c) external causes. The structure defines the topology of a network including links and nodes. The dynamics (i.e., traffic flow) defines what processes are happening on the network. The external causes (e.g., disasters and driver distraction) are the phenomena that impact either structure or dynamics. These principal components do tend to influence each other. For example, the collapse of a bridge (i.e., external cause) could render certain nodes and links (i.e., structure) ineffective thereby affecting traffic flow (i.e., dynamics). A distracted driver (i.e., external cause) on a road can also cause accidents that can negatively impact traffic flow. Thus, to model the performance and vulnerability of a network, it is necessary to consider such interactions among these principal components. The main objective of this research is to formalize and develop a computational framework that can: (a) predict the macroscopic performance of a transportation network based on its multiple structural and dynamical attributes (Chapter 2), (b) analyze its vulnerability as a result of man-made/natural disruption that minimizes network connectivity (Chapter 3), and (c) evaluate network vulnerability due to driver distraction (Chapter 4). An integrated framework to address these challenges---which have largely been investigated as separate research topics, such as distracted driving, infrastructure vulnerability assessment and traffic demand modeling---needs to simultaneously consider all three principal components (i.e., structure, dynamics, and external causes) of a network. In this research, the integrated framework is built upon recent developments (theories and methods) in interdisciplinary domains, such as network science, cognitive science and transportation engineering. This is the novelty of the proposed framework compared to existing approaches. Finally, the framework were validated using real-world data, existing studies and traffic simulated results. |
