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: 1545043
• 负责人: Ram Rajagopal
• 金额: $ 26万
• 依托单位: Stanford 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 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1545043&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.

在城市旅行中，停车会占用大量的旅行成本（时间和金钱）。因此，它可以在很大程度上影响旅行者对旅行方式、地点和时间的选择。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，可以利用停车对出行的影响，使交通系统更高效、更清洁、更有弹性。为了充分发挥停车在实现上述目标中的潜力，本文提出了一种网络物理停车社会系统。该网络物理系统由智能停车传感器、停车和交通数据存储库、停车管理系统和动态交通流控制组成。如果成功，调查结果将为管理停车创造一个新的范例，以减少交通拥堵、排放和燃料消耗，并增强系统的弹性。这些成果将通过出版物、讲习班和讨论会广泛传播。这项研究将为研究生和本科生提供跨学科的培训。这项研究的结果也填补了我们的研究生交通课程的空白，其中停车管理得到很少的报道。调查人员将在Coursera和美国国家公路研究所（National Highway Institute）组织一个在线短期培训课程，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教育项目，这些地图将在公共活动期间在博物馆画廊展出。从技术上讲，本研究将利用尖端传感技术、通信技术、大数据分析和反馈控制，开发新的理论、算法和系统，通过停车来有效管理交通基础设施。本研究通过对海量个性化和基于基础设施的交通和停车数据的研究，深入了解出行和停车行为，并建立了一种基于水库的网络流模型，为大规模交通网络中停车与交通流之间复杂的相互作用建模奠定了基础。该理论将在不同的粒度水平上进行研究，以揭示停车信息和定价机制如何影响私人和公共停车竞争市场中的网络流量。此外，本研究提出闭环控制机制，以提高城市网络的移动性和可持续性。对街道上和街道外公共停车场的价格、使用权和信息进行动态控制，以便：a)通过日常出行体验和/或在线信息系统改变所有通勤者的日常行为；B)改变一小部分适应性旅行者的出行行为，他们知道每天的停车时间信息并遵守建议；c)通过竞争性停车市场影响私人停车场的市场价格。