Mathematical Sciences: Measurement, Modeling and Prediction for Infrastructural Systems

小行星9313013 该国的有形基础设施，特别是地面运输，迫切需要恢复、改善和扩大。 经济资源不足以解决问题的所有方面，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性，建模其属性和预测其性能在各种情况下。 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IVHS当前实验计划中的实时路线规划;材料特性，重点关注混凝土路面的设计和行为以及混凝土性能特征和微观结构之间的基本联系;以及结合个人出行/活动行为的出行需求建模，以实现对各种政策情景下的需求的详细预测，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、统计和交通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战略。 这些方法将被转移到广泛的运输问题，依赖于模拟模型，实验室测试和现场性能数据的组合。

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.