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

• 批准号: 1907563
• 负责人: Srinivas Peeta
• 金额: $ 21.95万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1907563&HistoricalAwards=false
• 关键词: Collaborative; Research; Statistical; Learning; Driving

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.

对于那些依赖私人拥有或租用的汽车、出租车或公共交通工具上下班、购物和跑腿的人来说，拥堵是很熟悉的。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，目的是从中获得洞察力，揭示拥堵如何随着时间的推移而演变。不幸的是，这样的模型可能很容易变得如此庞大和复杂，以至于它们是笨拙的，并且需要简化，以便为乘客和驾驶员提供与路线选择和出发时间选择相关的准确和快速可计算的信息。为了实现这一目标，本项目将采用现代统计学、模拟实验和交通网络用户之间竞争可用道路容量的概念，以更好地描述和更有效地计算依赖道路网络的驾驶员的行为。这项研究的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策，政府组织可以更好地监管严重拥挤的主要大都市地区的出行决策。这项研究还将为进入运输业的学生提供系统级的体验式学习机会。具体而言，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城市道路网络寻求满足其出行需求时对可用路线和出发时间的非合作探索。预期研究的一个关键目标将是最流行的动态交通分配（DTA）类型的解决方案的有效计算，即所谓的动态用户平衡（DUE）。由于缺乏闭合形式的旅行延迟算子，使得DUE计算繁琐且缓慢。该计划是取代现有的，微分代数方程（DAE）系统表示行程延迟与封闭形式，近似延迟运营商的基础上的一种形式的统计学习称为克里格。基于这种方法的Ad hoc实验显示出对小型网络的巨大潜力，但并不确定。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。

项目成果

Hybrid route choice model incorporating latent cognitive effects of real-time travel information using physiological data

• DOI: 10.1016/j.trf.2021.05.021
• 发表时间: 2021-06-25
• 期刊: TRANSPORTATION RESEARCH PART F-TRAFFIC PSYCHOLOGY AND BEHAVIOUR
• 影响因子: 4.1
• 作者: Agrawal, Shubham;Peeta, Srinivas
• 通讯作者: Peeta, Srinivas