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

作为一种集体运输方式，过境往往会在人口稠密的地区蓬勃发展。不幸的是，自20世纪50年代以来，美国郊区的无序扩张使这种状况逐渐不利于交通发展。要扭转美国公共交通使用率下降的趋势，需要规划、政策制定和工程方面的共同努力。这一努力的关键是改变交通系统的设计和运营方式。为了在传统交通系统失效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，更紧密地迎合他们的需求，并高效运营，以最大限度地降低成本。该奖项调查了一种新的交通系统，称为网络使能的需求-交互式交通系统，它考虑了这些需求。从这个项目中获得的知识和见解有助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，提供了一个平台，让过境用户、机构和学生参与进来。这个项目中收集的乘客GPS痕迹将形成一个大型的高保真交通需求的众包数据库，这是今天所不存在的。这一丰富的新数据源将使研究人员、政策制定者和实践者受益。该项目的目标是创建理论和方法来表征、分析、操作和评估网络使能的需求-交互式交通系统。该系统可以被视为一个融合了传统固定路线服务和需求响应式公交的混合系统。研究活动在三个相互交织的推力下组织。第一个要点涉及拟议系统的特征和战略设计。第二个重点研究了由于需要运行该系统而产生的数学问题，这些问题是：(1)根据乘客请求确定按需车辆路线的车辆路线问题；(2)协调调度并根据需求调整服务的战术设计问题；以及(3)使用基于离散事件模拟平台和基于智能手机的交通旅行计划应用程序TransitGenie的数据驱动和基于模拟的框架为乘客生成最佳旅行指导的旅行计划问题。将使用案例研究评估不同的系统设计和业务政策，案例研究将根据从开放来源和当地运输机构获得的综合运输供需数据创建。TransitGenie将配备有从其用户那里收集未识别的GPS痕迹的能力。

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.