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Collaborative Research: Statistical Learning, Driving Simulator-Based Modeling, and Computationally Tractable Dynamic Traffic Assignment

合作研究：统计学习、基于驾驶模拟器的建模以及计算可处理的动态交通分配

基本信息

批准号：
1662968
负责人：
Terry Friesz
金额：
$ 24.6万
依托单位：
Pennsylvania State Univ University Park
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-01 至 2021-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1662968&HistoricalAwards=false
关键词：
Collaborative Research Statistical Learning Driving

项目摘要

Congestion is familiar to anyone who relies on a privately owned or rented automobile, taxi, or public transit for commuting, shopping and errand running. Historically, engineers and scientists exploring traffic networks frequently build mathematical models with the intent of coaxing from them insights revealing how congestion may evolve over time. Unfortunately, such models may easily become so large and complex that they are unwieldly, and simplifications are needed in order to provide passengers and drivers with accurate and rapidly computable information pertinent to route choice and departure time selection. Toward that goal, this project will employ modern statistics, simulation experiments, and notions of competition among traffic network users for available road capacity to better depict and more efficiently compute the behaviors of drivers who rely on road networks. The broader impacts of this research will be substantial. In particular, the results of this research will allow commuters and urban freight carriers to make more informed travel decisions, and governmental organizations to better regulate travel decisions within heavily congested major metropolitan regions. This study will also provide system-level experiential learning opportunities for students entering the transportation workforce. Specifically, through a combination of experiments and machine learning and model development, this project will aim to depict the noncooperative exploration of available routes and departure times by drivers and passengers seeking to fulfill their travel demands via metropolitan road networks. A key goal of the intended research will be the efficient computation of solutions to the most prevalent type of dynamic traffic assignment (DTA), namely so-called dynamic user equilibrium (DUE). It is the lack of closed-form travel-delay operators that makes DUE computation tedious and slow. The plan is to replace the existing, differential algebraic equation (DAE) system representing travel delay with closed-form, approximate delay operators based on a form of statistical learning known as Kriging. Ad hoc experiments based on such an approach show great promise for small networks, but are not definitive. The PIs will develop the envisioned models and make developed software available as free-ware or inexpensive apps.

拥堵对于任何依赖私人拥有或租赁的汽车、出租车或公共交通来通勤、购物和跑腿的人来说都是熟悉的。从历史上看，探索交通网络的工程师和科学家经常建立数学模型，目的是从他们那里获得揭示拥堵如何随着时间的推移而演变的见解。不幸的是，这样的模型可能很容易变得太大和太复杂，以至于它们不方便使用，需要进行简化，以便为乘客和司机提供与路线选择和出发时间选择相关的准确和快速的计算信息。为了实现这一目标，该项目将使用现代统计、模拟实验和交通网络用户之间对可用道路容量的竞争概念，以更好地描述和更有效地计算依赖道路网络的司机的行为。这项研究的更广泛影响将是巨大的。特别是，这项研究的结果将使通勤者和城市货运公司做出更明智的出行决定，并使政府组织能够更好地管理严重拥堵的主要大都市地区的出行决定。这项研究还将为进入交通运输劳动力的学生提供系统层面的体验式学习机会。具体地说，通过实验、机器学习和模型开发的结合，该项目旨在描述司机和乘客通过大都市公路网寻求满足其出行需求的非合作探索可用路线和出发时间。预期研究的一个关键目标将是有效地计算最流行类型的动态交通分配(DTA)的解，即所谓的动态用户平衡(DUE)。正是由于缺乏封闭形式的旅行延迟算子，使得适当的计算既乏味又缓慢。该计划是用基于一种称为克立格的统计学习形式的闭合形式的近似延误算子来取代现有的表示行程延误的微分代数方程(DAE)系统。基于这种方法的临时实验显示了小型网络的巨大前景，但并不是决定性的。PI将开发设想的模型，并将开发的软件作为免费软件或廉价应用程序提供。