详情

Machine Learning-Based High-Fidelity Mesoscopic Modeling Tool for Traffic Network Optimization

项目名称：	Machine Learning-Based High-Fidelity Mesoscopic Modeling Tool for Traffic Network Optimization
摘要：	This project explores using machine learning techniques to spatially and temporally customize predictive functions in queuing based macro simulations of traffic. Its objective is to replace much slower "one-size fits all" micro simulators so that reliable adaptive traffic control and optimization will be possible, which is a very practical end-goal. The first goal is to create machine learning algorithms for learning how to predict the travel time of a car on a specific segment which may include difficult segments which represent signaling such as intersections or toll booths. Data will ultimately come from car telemetry monitoring. The predictor functions will be further used in this project to make much more efficient, reliable, and location sensitive traffic simulator which is necessary for future optimization algorithms. The initial experiments will attempt to reproduce VISSIM output but much more efficiently. The second goal is then to create traffic optimization algorithms using the research team's simulator to estimate cost of a signaling strategy. The team will be using several algorithms they have used in the past for evacuation planning as a starting point.
状态：	Active
资金：	80000
资助组织：	Pacific Northwest Transportation Consortium<==>Office of the Assistant Secretary for Research and Technology
项目负责人：	Heckendorn, Robert
执行机构：	National Institute for Advanced Transportation Technology
开始时间：	20210516
预计完成日期：	20220515
主题领域：	Data and Information Technology；Highways；Operations and Traffic Management