Mathematical Sciences: Measurement, Modeling and Prediction for Infrastructural Systems

9313013卡尔国家的有形基础设施，特别是地面交通，急需修复、改善和扩大。经济资源不足以解决这一问题的所有方面，因此对拟议的对策进行合理评价至关重要。为此，必须部署方法来测量基础设施系统的特征，对其属性进行建模并预测其在各种情况下的性能。pi选择了一系列影响交通供需的关键问题：网络建模，智能车辆-公路系统的内在问题，特别关注当前IVHS实验方案中的实时路线规划；材料特性，重点关注混凝土路面的设计和行为，以及混凝土的性能特征和微观结构之间的基本联系；旅行需求建模将个人的旅行/活动行为结合起来，从而能够详细预测各种政策情景下的需求，并最终预测交通网络如何受到影响。对于系统，我们研究统计是表征和预测的使能技术。通过材料，区域，统计和运输科学家之间的跨学科合作，将制定短期解决方案和长期改进的战略。这些方法将适用于依赖于模拟模型、实验室测试和现场性能数据组合的各种运输问题。

9313013 Karr The nation's physical infrastructure, in particular, surface transportation, is in urgent need of restoration, improvement and expansion. Economic resources are insufficient to address all aspects of the problem, so that rational evaluation of proposed responses is crucial. To this end, methodology must be deployed for measuring characteristic of infrastructural systems, modeling their properties and predicting their performance under various scenarios. The PIs select a set of key problems affecting transportation supply and demand: network modeling, an intrinsic issue for Intelligent Vehicle-Highway Systems, with specific attention to real-time route planning in current experimental programs in IVHS; materials properties, with a focus on the design and behavior of concrete pavement as well as fundamental links between performance characteristics and microstructure of concrete; and travel demand modeling that incorporates travel/activity behavior of individuals to enable detailed predictions of demand under various policy scenarios and, eventually, how the transportation network is affected. For the systems, we study statistics is the enabling technology for characterization and prediction. Through cross- disciplinary collaboration among materials, regional, statistical and transportation scientists, strategies for short-term solutions and longer-term improvements will be developed. The methods will be transferable to a wide range of transportation problems that rely on a combination of simulation models, laboratory tests and field performance data.