Collaborative Research: Stochastic and Dynamic Hyperpath Equilibrium Models

合作研究：随机和动态超路径平衡模型

• 批准号: 1069284
• 负责人: Stephen Boyles
• 金额: $ 15.59万
• 依托单位: University of Wyoming
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2011-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1069284&HistoricalAwards=false
• 关键词: Collaborative; Research; Stochastic; Dynamic; Hyperpath

This grant provides funding for the development of adaptive hyperpath equilibrium models for stochastic and dynamic transportation networks. These models will capture both individual driver behavior and the equilibration process that occurs across multiple drivers, and will represent the heterogeneity in driver risk preferences. A three-phase approach is proposed that addresses the behavioral foundations, the analytical formulation of the hyperpath equilibrium, and the application to a network pricing problem. In the first phase of the proposed research, an interactive web game will be used to study the nature of adaptive routing behavior under multiple route choice scenarios. The second phase builds on these results by developing a variational inequality formulation to represent the routing behavior. The final phase applies this model to develop adaptive congestion pricing strategies.If successful, the results of this research will result in a fundamentally new transportation planning tool that can assess the impacts of travel information tools. The fundamental idea is that travelers could use this information to change their driving behavior en route, perhaps avoiding congested roadways based on the information received. The research will model the system-level effects of many travelers receiving information and behaving in this way, keeping in mind differences in their travel purposes and risk tolerance. This model will also be used to evaluate how dynamic tolls should be adjusted in response to system disruptions. Accomplishing these goals will provide transportation planners with the tools to implement these technologies in a way which is most beneficial to the traveling public. This grant will provide the opportunity to expose multiple graduate students and under-graduate researchers to cutting edge research in large-scale network modeling, stochastic optimization and transportation economics. The PI?s will also interact with several local community organizations and educational programs to disseminate the results and insights to a wider audience.

该补助金为随机和动态交通网络的自适应超路径平衡模型的开发提供资金。这些模型将捕捉个体驾驶员行为和多个驾驶员之间发生的平衡过程，并将代表驾驶员风险偏好的异质性。一个三阶段的方法，提出了解决的行为基础，分析制定的超路径均衡，并应用到网络定价问题。在建议的研究的第一阶段，一个互动的网络游戏将被用来研究多个路由选择场景下的自适应路由行为的性质。第二阶段建立在这些结果的基础上，通过开发一个变分不等式公式来表示路由行为。最后一个阶段应用该模型开发自适应拥挤定价策略，如果成功，本研究的结果将导致一个全新的交通规划工具，可以评估出行信息工具的影响。 其基本思想是，旅行者可以使用这些信息来改变他们在途中的驾驶行为，也许可以根据收到的信息避开拥挤的道路。这项研究将模拟许多旅行者以这种方式接收信息和行为的系统级影响，同时考虑到他们旅行目的和风险承受能力的差异。 该模型还将用于评估应如何调整动态收费以应对系统中断。实现这些目标将为交通规划者提供工具，以最有利于旅行公众的方式实施这些技术。该补助金将提供机会，让多名研究生和本科生研究人员接触大规模网络建模，随机优化和运输经济学的前沿研究。私家侦探？s还将与几个当地社区组织和教育项目互动，向更广泛的受众传播结果和见解。