Efficient Calibration Techniques for Stochastic Traffic Simulators

本项目为随机微观多智能体城市交通模型的标定开发了高效的优化算法。效率是通过利用问题结构和结合两个已经高度发展的领域的思想来实现的，这两个领域是基于仿真的优化（SO）的数学学科和根据真实传感器数据校准交通仿真模型的应用学科。作为研究的一部分，设计了元模型SO技术，建立了基于解析概率交通模型的高效元模型，并提出了小样本问题的点选择技术。这种技术适用于在紧张的模拟预算内解决复杂的校准问题。它们响应交通模拟用户的需求，允许他们以实用的方式解决复杂的问题。如果成功，该项目的技术将使交通模拟器得到更准确和有效的校准，从而产生更可靠的结果。这一点很重要，因为联邦、州、区域和地方运输机构以及运输机构和各种运输顾问开发并依靠微观模拟工具来确定网络设计或交通管理战略，以减轻拥堵及其对经济、环境和健康的负面影响。该奖项将与一个区域规划机构合作执行。这使我们能够根据从业者当前和未来的需求来设计技术。此外，还将对柏林市进行大规模的案例研究。通过在复杂的大规模案例研究中测试这些工具的性能，我们将展示我们提出的运输实践方法的好处。这个项目将招收本科少数民族学生。它将把它的研究结果整合到高级研究生的交通和运筹学研究科目中。

This project develops efficient optimization algorithms for the calibration of stochastic microscopic multi-agent urban traffic models. Efficiency is achieved by exploiting problem structure and by bringing together ideas from two already highly developed fields, the mathematical discipline of simulation-based optimization (SO) and the applied discipline of calibrating traffic simulation models from real sensor data. As part of the research, metamodel SO techniques are designed, efficient metamodels are formulated based on analytical probabilistic traffic models and point selection techniques for small-sample size problems are proposed. Such techniques are suitable to address complex calibration problems within a tight simulation budget. They respond to the needs of transportation simulation users by allowing them to address complex problems in a practical manner.If successful, the techniques of this project will allow traffic simulators to be more accurately and efficiently calibrated, leading to more reliable results. This is important given that federal, state, regional and local transportation agencies, as well as transit agencies and a variety of transportation consultants develop and rely on microscopic simulation tools to identify network design or traffic management strategies that mitigate congestion as well as its negative economic, environmental and health impacts. This award will be carried out in collaboration with a regional planning agency. This allows us to design techniques informed by the current and future needs of practitioners. Additionally, a large-scale case study of the city of Berlin will be carried out. By testing the performance of these tools on complex large-scale case studies, we will demonstrate the benefits of our proposed approach to transportation practice. This project will engage undergraduate minority students. It will integrate its findings within advanced graduate transportation and operations research subjects.