Collaborative Research: CybeR-Enabled Demand-Interactive Transit for the Next-Generation Transportation Systems

作为一种集体运输方式，公共交通往往在人口密集地区蓬勃发展。在美国，不幸的是，自20世纪50年代以来，郊区的蔓延已经使条件逐渐不利于公共交通的发展。 要扭转美国公交使用率下降的趋势，需要规划、决策和工程方面的共同努力。这一努力的关键是改变过境系统的设计和运营方式。为了在传统交通系统无法满足的低密度地区取得成功，下一代交通系统必须更好地与乘客互动，更紧密地满足他们的需求，并有效地运营以最大限度地降低成本。 该奖项研究了一种新型的交通系统，称为CybeR启用的需求交互式交通系统，该系统考虑到了这些需求。 从该项目中获得的知识和见解有利于下一代交通系统的设计和实施。 拟议的评价框架包括一个基于智能手机的过境路线和数据收集工具，为过境用户、机构和学生提供了一个平台。该项目收集的乘客GPS轨迹将形成一个大型的众包数据库，其中包含当今不存在的高保真交通需求。这个新的数据源的财富将有利于研究人员，政策制定者和practitioners.The项目的目标是创建理论和方法来表征，分析，操作，并评估CybeR使能的需求交互式交通系统。这一系统可以被视为一个混合系统，它将传统的固定路线服务和需求响应型交通相结合。研究活动是在三个相互交织的主旨下组织的。第一个重点是拟议系统的特点和战略设计。第二个重点研究了系统运行所需的数学问题，包括：（1）根据乘客需求确定按需车辆行程的车辆路径问题，（2）协调调度和适应需求的服务的战术设计问题，（3）根据乘客需求确定车辆路径问题，（4）根据乘客需求确定车辆路径问题，（5）根据乘客需求确定车辆路径问题。以及（3）行程规划问题，其使用基于离散-事件模拟平台和基于智能手机的公交出行规划应用程序TransitGenie。将利用案例研究对不同的系统设计和业务政策进行评估，这些案例研究将根据从公开来源和地方过境机构获得的综合过境供需数据创建。TransitGenie将配备仪器，能够从其用户那里收集去识别的GPS轨迹。

Being a mode of collective transport, transit tends to thrive in densely populated areas. In the US, unfortunately, suburb sprawl has made the condition progressively unfavorable for transit development since the 1950s. To reverse the trend of declining transit use in the US requires a concerted effort of planning, policy-making and engineering. Critical to this effort is to transform the way by which transit systems are designed and operated. In order to succeed in lower density areas where traditional transit systems fail, the next-generation transit systems must better interact with passengers, more closely cater to their demands, and operate efficiently to minimize costs. This award investigates a new kind of transit system called the CybeR-Enabled Demand-Interactive Transit system that takes into account these needs. The knowledge and insights gained from this project benefits the design and implementation of next-generation transit systems. The proposed evaluation framework, including a smartphone-based transit routing and data-collection tool, offers a platform to engage transit users, agencies and students. The passenger GPS traces collected in this project will form a large crowd-sourced database of high-fidelity transit demands that does not exist today. This wealth of the new data source will benefit researchers, policy-makers and practitioners.The objective of this project is to create theories and methods to characterize, analyze, operationalize, and evaluate the CybeR-Enabled Demand-Interactive Transit system. This system can be viewed as a hybrid system that integrates traditional fixed-route service and demand-responsive transit. The research activities are organized under three interwoven thrusts. The first thrust addresses the characterization and strategic design of the proposed system. The second thrust investigates mathematical problems arising from the need to operationalize the system which are: (1) the vehicle routing problem that determines the on-demand vehicle itineraries according to passenger requests; (2) the tactical design problem that coordinates schedules and adapts services to demands; and (3) the trip planning problem that generates optimal trip guidance for passengers using a data-driven and simulation-based framework that is based on a discrete-event simulation platform and a smartphone-based transit trip planning application known as TransitGenie. Different system designs and operational policies will be evaluated using case studies, which will be created from synthesized transit supply and demand data obtained from open sources and local transit agencies. TransitGenie will be instrumented with the ability to collect de-identified GPS traces from its users.