SBIR Phase I: Accurate and Scalable Traffic Estimation and Traffic-Aware Routing

SBIR 第一阶段：准确且可扩展的流量估计和流量感知路由

• 批准号: 0945248
• 负责人: Lewis Girod
• 金额: --
• 依托单位: TrafficRNA
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-01-01 至 2010-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0945248&HistoricalAwards=false
• 关键词: SBIR; Phase; Accurate; Scalable; Traffic

This Small Business Innovation Research Phase I project will develop a system that has the potential to reduce the amount of time and fuel spent by people in road traffic. Leveraging academic research results, this system will incorporate three technologies to provide an end-to-end solution to the traffic problem: 1) software to gather position data from mobile smartphones and a method to process massive amounts of this noisy data (in conjunction with existing bulk traffic data) to produce accurate traffic estimates in real-time, 2) a method to predict traffic delay distributions on road segments using probabilistic graphical models, and 3) a traffic-aware routing engine that uses an efficient and dynamic stochastic routing algorithm, which provides routes that reliably maximize the probability of arriving within a deadline. Traffic is a significant societal problem that affects people on a regular basis. The existing ecosystem for traffic solutions is both complex and inefficient, but the massive disruption being caused by mobile smartphones has the potential to change this in the next few years. The current market demand for traffic subscription services and their renewal rates validates the market for more advanced traffic-aware applications. As an operator of a mobile web service and a provider of the key enabling technologies, the company plans to build revenue by: 1) an ad-supported and value-added service model for consumers running a mobile application, 2) technology licensing to strategic partners, and 3) public/private partnerships with regional governments. The first model provides direct customer access with a predictable and controllable revenue stream. The second and third models provides a large data collection footprint and significant upside penetration of services. If successful, the company has the potential to provide significant societal benefit.

这个小型企业创新研究第一阶段项目将开发一种系统，该系统有可能减少人们在道路交通中花费的时间和燃料。利用学术研究成果，该系统将结合三项技术来提供端到端的交通问题解决方案：1)从移动智能手机收集位置数据的软件和处理大量此类噪声数据的方法(与现有的大量交通数据结合)，以实时产生准确的交通估计；2)使用概率图形模型预测路段上交通延误分布的方法；以及3)使用高效和动态随机路由算法的交通感知路由引擎，该引擎提供可靠地最大化在最后期限内到达的概率的路线。交通是一个重大的社会问题，经常影响人们。现有的交通解决方案生态系统既复杂又低效，但移动智能手机造成的巨大颠覆有可能在未来几年改变这一点。目前市场对流量订阅服务的需求及其续约率验证了更先进的流量感知应用的市场。作为移动网络服务的运营商和关键支持技术的提供商，该公司计划通过以下方式创造收入：1)为运行移动应用程序的消费者提供广告支持和增值服务模式；2)向战略合作伙伴发放技术许可；3)与地区政府建立公私合作伙伴关系。第一种模式通过可预测和可控的收入流提供直接客户访问。第二和第三种模式提供了大量的数据收集占用空间和显著的服务上行渗透率。如果成功，该公司有可能提供巨大的社会效益。