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

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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.