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

无论是在正常情况下还是在极端情况下，都需要了解交通网络的状态，例如网络级的交通流量和行驶时间，以便进行网络监控和有效的交通系统管理和控制。最近的技术进步，如移动传感和联网汽车，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，通过整合复杂网络结构上的各种交通数据片段来估计交通流量和旅行时间的统计特性（例如由固定传感器收集的交通数量，以及由蓝牙标签读取器日志、手机记录和全球定位系统痕迹收集的个人“城市数字足迹”）。该项目的成功成果将通过更有效地利用信息，从而更可持续和高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指导活动，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。在数学上，这个项目解决的问题是：给定直接可测量的网络参数x，这些参数往往是局部的和不完整的，如何推断通常难以直接测量的全局网络参数y， y和x之间的映射建立在一个复杂的网络结构上？这类问题在交通、通信和能源网络中有着广泛的应用；虽然，这个项目的重点是交通网络。研究团队将以交通网络科学、随机优化、变分分析和非参数估计为基础，开展以下主要工作：(1)建立基于随机优化和非参数估计的交通网络识别优化框架；(2)通过约束和功能映射将多源交通数据（硬信息）与基于领域知识的软信息集成；(3)通过贝叶斯方法，将基于历史数据的离线估计与实时信息相结合，实现在线估计和决策支持；(4)使用真实世界的数据和计算机模拟测试和验证项目的方法。本研究将交通网络科学与数据分析相结合，建立统一的交通网络识别理论框架。通过在处理各种类型的硬信息和软信息以及捕获交通网络物理方面提供比现有方法更大的建模灵活性，这种新方法在交通网络系统识别方面带来了范式转变。该项目还创建了一个现实世界的工程平台，以加强优化与统计之间的联系。

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.