CPS: Synergy: Collaborative Research: Matching Parking Supply to Travel Demand towards Sustainability: a Cyber Physical Social System for Sensing Driven Parking

CPS：协同：协作研究：将停车供应与出行需求相匹配，实现可持续发展：传感驱动停车的网络物理社会系统

• 批准号: 1544826
• 负责人: Sean Qian
• 金额: $ 28万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-15 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1544826&HistoricalAwards=false
• 关键词: CPS; Synergy; Collaborative; Research; Matching

Parking can take up a significant amount of the trip costs (time and money) in urban travel. As such, it can considerably influence travelers' choices of modes, locations, and time of travel. The advent of smart sensors, wireless communications, social media and big data analytics offers a unique opportunity to tap parking's influence on travel to make the transportation system more efficient, cleaner, and more resilient. A cyber-physical social system for parking is proposed to realize parking's potential in achieving the above goals. This cyber-physical system consists of smart parking sensors, a parking and traffic data repository, parking management systems, and dynamic traffic flow control. If successful, the results of the investigation will create a new paradigm for managing parking to reduce traffic congestion, emissions and fuel consumption and to enhance system resilience. These results will be disseminated broadly through publications, workshops and seminars. The research will provide interdisciplinary training to both graduate and undergraduate students. The results of this research also fills a void in our graduate transportation curriculum in which parking management gets little coverage. The investigators will organize an online short training course in Coursera and National Highway Institute to bring results to a broader audience. The investigators will also collaborate with Carnegie Museum of Natural History to develop an online digital map and related educational programs, which will be presented in the museum galleries during public events.Technically, new theories, algorithms and systems for efficient management of transportation infrastructure through parking will be developed in this research, leveraging cutting-edge sensing technology, communication technology, big data analytics and feedback control. The research probes massive individualized and infrastructure based traffic and parking data to gain a deeper understanding of travel and parking behavior, and develops a novel reservoir-based network flow model that lays the foundation for modeling the complex interactions between parking and traffic flow in large-scale transportation networks. The theory will be investigated at different levels of granularity to reveal how parking information and pricing mechanisms affect network flow in a competitive market of private and public parking. In addition, this research proposes closed-loop control mechanisms to enhance mobility and sustainability of urban networks. Prices, access and information of publicly owned on-street and off-street parking are dynamically controlled to: a) change day-to-day behavior of all commuters through day-to-day travel experience and/or online information systems; b) change travel behavior of a fraction of adaptive travelers on the fly who are aware of time-of-day parking information and comply to the recommendations; and c) influence the market prices of privately owned parking areas through a competitive parking market.

停车可能会占用城市旅行的大量旅行费用（时间和金钱）。因此，它可以极大地影响旅客对模式，位置和旅行时间的选择。智能传感器，无线通信，社交媒体和大数据分析的出现提供了一个独特的机会，可以利用停车场对旅行的影响，以使运输系统更高效，更清洁且更具弹性。有人提出了一个用于停车的网络物理社会系统，以意识到停车实现上述目标的潜力。 该网络物理系统由智能停车传感器，停车位和交通数据存储库，停车管理系统以及动态的交通流量控制组成。如果成功，调查的结果将创建一个新的范式，用于管理停车，以减少交通拥堵，排放和燃油消耗并增强系统的弹性。这些结果将通过出版物，研讨会和研讨会广泛传播。这项研究将为研究生和本科生提供跨学科的培训。这项研究的结果还填补了我们的研究生运输课程中的空白，在该课程中，停车管理几乎没有承保范围。调查人员将在Coursera和National Highway Institute举办在线短培训课程，以将结果带给更广泛的受众。调查人员还将与卡内基自然历史博物馆合作开发在线数字地图和相关的教育计划，该计划将在公众活动期间在博物馆画廊中介绍。技术，新理论，算法和系统，用于通过这项研究中开发通过停车的运输基础设施的有效管理，在这项研究中开发，利用高级的技术感官技术，交流技术，交流技术，大型数据回报，大型数据访问了大型数据回报。该研究探究了基于个性化和基础设施的大量基础交通和停车数据，以更深入地了解旅行和停车行为，并开发了一种基于储层的新型网络流量模型，该模型为大规模运输网络中的停车与交通流量和交通流量之间的复杂交互作用奠定了基础。该理论将在不同级别的粒度上进行研究，以揭示停车信息和定价机制如何影响私人和公共停车市场的竞争市场中的网络流。此外，这项研究提出了闭环控制机制，以增强城市网络的流动性和可持续性。价格，访问和信息的街道上和街外停车场的访问和信息受到动态控制： b）更改即时了解日期停车信息并遵守建议的自适应旅行者的旅行行为； c）通过竞争性停车市场影响私人停车场的市场价格。