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Analytic queueing network models often assume infinite capacity for all queues. For real systems this infinite capacity assumption does not hold, but is often maintained due to the difficulty of grasping the between-queue correlation structure present in finite capacity networks. This correlation structure helps explain bottleneck effects and spillbacks,(More)
The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. This paper proposes a simulation-based optimization (SO) method that enables the efficient use of complex stochastic urban traffic simulators to address various transportation problems. It presents a metamodel that integrates information(More)
This paper uses a simulation-based optimization framework to investigate the added value of using higher-order distributional information from detailed traffic simulators to address signal control problems. In particular, this paper uses higher-order travel time distributional information to derive signal plans that account for travel time reliability. In(More)
This paper applies a computationally efficient simulation-based optimization (SO) algorithm suitable for large-scale transportation problems. The algorithm is based on a metamodel approach. The metamodel combines information from a high-resolution yet inefficient microscopic urban traffic simulator with information from a scalable and tractable analytical(More)
We propose a new Financial Condition Index (FCI) for Asian economies based on two different methodologies: a VAR model and a Dynamic Factor Model. The paper shows that this index has predictive power in forecasting GDP growth and may be thus used as a leading indicator. Based on the FCI, financial conditions in Asia tightened substantially earlier in the(More)
The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Abstract Traditional queueing network models assume infinite queue capacities due to the complexity of capturing interactions between finite capacity queues. Accounting for this correlation can help explain how congestion propagates through(More)
We present a dynamic network loading model that yields queue length distributions, accounts for spillbacks, and maintains a differentiable mapping from the dynamic demand on the dynamic queue lengths. The model also captures the spatial correlation of all queues adjacent to a node, and derives their joint distribution. The approach builds upon an existing(More)