Efficient Calibration Techniques for Stochastic Traffic Simulators

随机交通模拟器的高效校准技术

• 批准号: 1334304
• 负责人: Carolina Osorio
• 金额: $ 30.47万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1334304&HistoricalAwards=false
• 关键词: Efficient; Calibration; Techniques; Stochastic; Traffic

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.

本计画针对随机微观多智能体城市交通模型的校正，发展有效的最佳化演算法。效率是通过利用问题结构，并通过汇集来自两个已经高度发达的领域，基于仿真的优化（SO）的数学学科和校准交通仿真模型从真实的传感器数据的应用学科的想法。作为研究的一部分，元模型SO技术的设计，有效的元模型制定的基础上分析概率交通模型和点选择技术的小样本问题提出。这种技术适合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，使他们能够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能够更准确和有效地校准，从而获得更可靠的结果。这一点很重要，因为联邦、州、地区和地方交通机构以及运输机构和各种交通顾问开发并依赖微观模拟工具来确定缓解拥堵及其负面经济、环境和健康影响的网络设计或交通管理策略。该奖项将与一个区域规划机构合作进行。这使我们能够根据从业者当前和未来的需求设计技术。此外，还将对柏林市进行大规模的案例研究。通过测试这些工具在复杂的大规模案例研究中的性能，我们将展示我们所提出的方法对运输实践的好处。这个项目将吸引少数民族本科生。它将把它的研究结果整合到高级研究生运输和运筹学科目中。