Collaborative Research: Transportation Network Identification: Information Fusion via Stochastic Optimization

交通网络状态的知识，例如，网络级的交通流量和旅行时间，是非常需要的网络监控和有效的运输系统管理和控制在正常和极端条件下。 最近的技术进步，如移动的传感和联网车辆，可以产生大数据。 总的来说，本研究旨在解决如何最好地利用这些固有的大规模，动态和异构（多源）数据流的挑战。 更具体地说，该项目寻求一种创新的系统方法，通过在复杂的网络结构上整合各种交通数据（如固定传感器收集的交通计数和蓝牙标签阅读器日志收集的个人“城市数字足迹”，手机记录和全球定位系统跟踪）来估计交通流量和旅行时间的统计特性。该项目的成功结果将通过更有效地利用信息，从而更可持续和更有效地规划和运营交通系统，直接造福社会。 该项目包括课程开发和学生辅导活动，帮助下一代交通专业人员更好地应对大数据时代带来的挑战。从数学上讲，该项目解决的问题是：给定可直接测量的网络参数x，其往往是局部的和不完整的，如何能够推断出通常难以直接测量的全局网络参数y，y和x之间的映射建立在复杂的网络结构上？这类问题在交通、通信和能源网络中有广泛的应用;尽管本项目的重点是交通网络。基于交通网络科学、随机优化、变分分析和非参数估计等方面的知识，研究团队将开展以下主要工作：（1）建立基于随机优化和非参数估计的网络识别优化框架;（2）多源交通数据整合（硬信息）与基于领域知识的软信息通过约束和功能映射;（3）通过贝叶斯方法将基于历史数据的离线估计与用于在线估计和决策支持的实时信息联系起来;（4）使用真实世界数据和计算机模拟测试和验证项目方法。 这项研究为交通网络识别建立了一个统一的理论框架，整合了交通网络科学和数据分析的知识。通过提供更大的建模灵活性比现有的方法在处理各种类型的硬和软信息，并在捕获交通网络物理，这种新方法迎来了交通网络系统识别的范式转变。该项目还创建了一个现实世界的工程平台，以加强优化和统计之间的联系。

Knowledge of the traffic network state, in terms of, for example, network-level traffic flow and travel time, is critically needed for network monitoring and effective transportation system management and control under both normal and extreme conditions. Recent technological advances, such as mobile sensing and connected vehicles, can generate big data. In general, this research aims to address challenges on how to best use these inherently large-scale, dynamic, and heterogeneous (multi-source) data streams. More specifically, the project seeks an innovative systems approach for estimating the statistical properties of traffic flow and travel time via integrating various traffic data pieces over a complex network structure (such as traffic counts collected by fixed sensors and individual "urban digital footprints" collected by Bluetooth tag reader logs, cellular phone records, and global positioning system traces). A successful outcome of this project will directly benefit society through more effective utilization of information, and thus more sustainable and efficient transportation system planning and operations. This project includes curricula development and student mentoring activities that help better prepare next-generation transportation professionals for challenges brought by the big data era.Mathematically, the question addressed in this project is: Given directly measurable network parameters x, which tend to be localized and incomplete, how can one infer global network parameters y that are often difficult to be measured directly, with the mapping between y and x built on a complex network structure? This problem category has broad applications in transportation, communication, and energy networks; although, the focus in this project is on transportation networks. Built on knowledge in transportation network science, stochastic optimization, variational analysis, and non-parametric estimation, the research team will pursue the following main tasks: (1) Creation of an optimization framework for network identification based on stochastic optimization and non-parametric estimation; (2) Integration of multi-source traffic data (hard information) with domain-knowledge-based soft information via constraints and functional mapping; (3) Linking historical-data-based offline estimation with real-time information for online estimation and decision support through Bayesian methods; (4) Testing and validating the project's methods using both real-world data and computer simulations. This research establishes a unifying theoretical framework for traffic network identification that integrates knowledge in transportation network science and data analytics. By providing greater modeling flexibility than existing methods in handling various types of hard and soft information and in capturing transportation network physics, this new method ushers a paradigm-shift in traffic network system identification. The project also creates a real-world engineering platform for strengthening connections between optimization and statistics.