SBIR Phase II: The Delivery of Content-Rich Traffic Information to Improve Driver Decision Making

该小型企业创新研究(SBIR)第二阶段项目将开发必要的用户界面、路线选择算法和驾驶员通知系统，以向旅途中的旅行者提供内容丰富的交通信息。公共和私人机构正在收集大范围内不同类型的大量交通数据。为了在旅行中对司机有用，这些数据必须减少到少量的信息，并以一种允许在最大限度地分散注意力的情况下易于理解的方式传递。受益于交通信息的关键司机行为是出行前出发时间的改变，出行前和途中路线的改变，以及通过司机知道估计到达时间来减少途中的焦虑。这些行为依赖于收集和分析在不断变化的交通条件下的计划路线，并将该路线与可能的更好的替代方案进行比较。这项研究将开发用户界面，从司机那里收集起点、目的地和路线信息，通过网络收集出行前信息，通过手机收集路线信息。将通过分析现场收集的路线数据来制定确定替代路线的算法。将开发和测试以最小干扰呈现显著信息的通知方法。这项研究将导致开发出更好的交通信息服务，真正支持司机在出行时做出的决定。这项研究的结果可能会对社会产生广泛的影响。交通拥堵是美国城市日益严重的问题。在一些地区，它已经成为经济增长的限制因素。近年来，重点已从提供额外能力转向更好地利用现有基础设施。以适合做出最优路线和出行决策的形式广泛传播交通信息，可以基于许多司机的分散决策来提高效率。根据交通信息修改出行，每年可为司机节省约39亿美元的生产力损失、2.25亿小时的旅行时间和3.4亿加仑的燃料。人们相信，这样的节省可以支持一个可行的个性化交通信息商业市场。通过改进送货路线、准时送货和更高效的调度，商务旅行也可能节省类似的费用。公共机构的拥堵管理通过将驾车者转移到不那么拥堵的道路上，努力有效地利用公共基础设施，并将受益于收集的交通数据和道路上的个别司机之间更好的接口。对路线选择的考察将促进对司机如何选择路线以及他们如何在变化的外部条件下改变路线的科学理解。

This Small Business Innovation Research (SBIR) Phase II project will develop user interfaces, routing algorithms, and driver notification systems necessary to deliver content-rich traffic information to travelers en route. Large volumes of traffic data, of varying types over large areas, is being gathered by public and private agencies. To be useful to a driver while traveling, this data must be reduced to small amounts of information and delivered in a way that allows easy comprehension with minimal distraction. Key driver behaviors benefiting from traffic information are pre-trip departure time changes, pre-trip and en-route route changes, and en-route anxiety reduction through drivers knowing the estimated arrival time. These behaviors depend on collecting and analyzing the planned route under changing traffic conditions and comparing that route with possible better alternatives. This research will develop user interfaces to collect origin, destination, and route information from drivers, pre-trip via the web and en-route via cell phone. Algorithms to determine alternate routes will be developed through analysis of field collected route data. Notification methods that present the salient information with minimal distraction will be developed and tested. The research will result in the development of better traffic information services that truly support the decisions drivers make as they travel.The results of this research have potentially broad impacts on society. Traffic congestion is a growing problem in U.S. cities. In some areas, it has become a limiting factor on economic growth. Emphasis has shifted in recent years from providing additional capacity to better utilization of the existing infrastructure. Broad dissemination of traffic information in a form suitable for making optimal routing and trip decisions allows efficiency improvements based on the decentralized decisions of many drivers. Trip modifications based on traffic information can save drivers an estimated $3.9 billion in lost productivity, 225 million hours of travel time, and 340 million gallons of fuel, per year. It is believed that such savings could support a viable commercial marketplace for personalized traffic information. Similar savings are possible for commercial travel through improvements in delivery routing, on-time delivery, and more efficient dispatching. Congestion management by public agencies strives for efficient use of the public infrastructure by shifting motorists onto less congested roads and would benefit from better interfaces between the traffic data collected and the individual drivers on the roads. The examination of route choice will advance the scientific understanding of how drivers choose their routes and how they alter those routes under changing external conditions.