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

该小型企业创新研究(SBIR)第一阶段项目是对向旅途中的旅行者提供内容丰富的交通信息所需的数据简化流程和人机界面进行的调查。公共和私人机构正在收集大量的交通数据，这些数据的类型很多，覆盖的区域很大。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。这项研究建议分析驾驶员对交通信息的需求，以确定一套面向任务和目标的行为，这些行为可以通过高质量的交通信息来改善。这组任务和目标将确定信息过滤环境，该环境将允许将非常集中的交通信息规范和带宽高效地传输到车载个人计算设备。将开发界面设计，利用现有技术呈现特定于上下文的信息。这项研究的结果可能对社会产生广泛的影响。它们将推动更好的交通信息服务的发展，真正支持司机在出行时做出的决定。基于这些接口的应用程序可以改善个人的路由行为、送货车队运营和拥堵管理。在美国大多数城市，交通拥堵是一个日益严重的问题。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。基于实时交通数据的出行修改每年可为个人司机节省约39亿美元的时间损失、2.25亿小时的旅行时间和3.4亿加仑的燃料。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。公共机构使用的许多拥堵管理策略可以受益于收集的交通数据和道路上的个人司机之间更好的接口。

This Small Business Innovative Research (SBIR) Phase I project is an investigation of the data reduction processes and human/computer interfaces necessary to deliver content-rich traffic information to travelers en route. Large volumes of traffic data, of many types and 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. This research proposes to analyze drivers' needs for traffic information to determine a set of task- and goal-oriented behaviors that can be improved by high-quality traffic information. The set of tasks and goals will determine information-filtering contexts that will allow the specification and bandwidth-efficient transmission of very focused traffic information to in-vehicle personal computing devices. Interface designs will be developed that present the context-specific information using available technologies.The results of this research have potentially broad impacts on society. They will drive the development of better traffic information services that truly support the decisions drivers make as they travel. Applications based on these interfaces can improve individual routing behavior, delivery fleet operations and congestion management. Traffic congestion is a growing problem in most 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 real-time traffic data can save individual drivers an estimated $3.9 billion in lost time, 225 million hours of travel time, and 340 million gallons of fuel, per year. Similar savings are possible for commercial travel, as well, through improvements in delivery routing, on-time delivery and more efficient dispatching. Many congestion management strategies used by public agencies could benefit from better interfaces between the traffic data collected and the individual drivers on the roads.