Collaborative Research: Traffic Congestion: Actions and Reactions

这项跨学科研究的重点是交通拥堵的影响，以及评估管理交通服务需求的替代投资，以及这些投资的社会和行为影响。今天，交通流量继续迅速超过社会可用于改善交通基础设施的资源，更好地管理现有设施将是解决这一问题的一种成本效益高的方法。由于拥堵取决于交通当局选择的政策，以及作为司机和乘客的个人的社会互动，分析拥堵需要一种综合的方法，允许不同的观点：运输机构的观点，消费者的观点，以及整个社会的观点。本研究的主要目标是找到一种最优的策略组合，通过更好地利用现有资源，将交通拥堵分散在地理空间和时间上，从而降低其成本。这些策略，如动态定价或高速公路保留方法，要么在美国少数选定的地区实施，要么在概念上进行了讨论；然而，它们对旅行和城市基础设施的影响尚不清楚。我们建议将经济学、系统工程学和交通工程学的工具结合起来，对拥堵问题进行全面分析。本研究将同时追求多个目标，包括用户交通服务的人工智能模型、评估不同交通政策的一系列经济实验、社会网络和交通选择相互作用的模型、交通和社会网络及其动态演变的系统动力学分析。这项研究的成功完成将有可能导致交通分析的新范例，并使人们对交通、城市基础设施和最终区域经济之间的联系有新的认识。

This interdisciplinary research focuses on the effects of traffic congestion, and on evaluating alternative investments to manage the demand for transport services, as well as the social and behavioral impact of these investments. Today traffic flows continue to rapidly outpace resources available to society for improving the transportation infrastructure, and better management of existing facilities would be a cost effective way for resolving the problem. Since congestion depends on the policies chosen by the traffic authorities, and on the social interaction of individuals as drivers and passengers, analyzing congestion requires an integrated approach that allows for different viewpoints: the transportation agencies', the consumers', as well as the perspective of society as a whole. The primary objective of this research is to find an optimal mix of strategies that will spread traffic congestion over geographical space and time by better utilization of current resources, thereby reducing its costs. These strategies, such as dynamic pricing or highway reservation methods, have either been implemented in few selected areas in the United States, or have been discussed conceptually; however, their effects on travel and the urban infrastructure are not well understood. We propose to bring together tools from economics, systems engineering, and transportation engineering for a comprehensive analysis of the congestion problem. A number of concurrent objectives will be pursued in this research, which involves artificial intelligence models of users' transportation services, a set of economic experiments to evaluate different transportation policies, a model of the interaction of social networks and transportation choices, and a system dynamics analysis of transportation and social networks and their dynamic evolution. The successful completion of this research will have the potential to lead to new paradigms of traffic analysis and to a new understanding of the linkages between transportation, the urban infrastructure and ultimately the regional economy.