Date of Award
Master of Science (MS)
Transit systems are an integral part of surface transportation systems. A connected vehicle technology (CVT) supported transit system will assist the users to manage trips both dynamically and efficiently. The primary focus of this research is to develop and evaluate the performance of a secure, scalable, and resilient data exchange framework. In the developed data exchange framework, a new data analytics layer, named Transit Cloud, is used to receive data from different sources, and send it to different users for a Dynamic Transit Operations (DTO) application. The DTO application allows the transit users to request trip information and obtain itineraries, using their personal information devices, (e.g., cell phone), and provides dynamic routing and scheduling information to the transit operators. A case study was conducted to investigate the effectiveness of the developed data exchange framework, by comparing the framework with the USDOT recommended data delivery delay requirements. This data exchange framework was simulated in the CloudLab, a distributed cloud infrastructure, in which, the data exchange delay for DTO was examined for different simulation scenarios, utilizing the synthetic data generated from Connected Vehicle Reference Implementation Architecture (CVRIA) and Research Data Exchange (RDE). Security, scalability, and resiliency of the developed data exchange framework are illustrated in this thesis. The results from the simulation network reveal that the data exchange delay satisfies the USDOT data delivery delay requirements. This suggests that the developed secure, scalable, and resilient data exchange framework, which is presented in this study, meets the application performance requirements. Thus, Transit Cloud is a more preferable alternative than the existing framework because of its added benefits in terms of security, scalability, and resiliency.
An, Yucheng, "A Robust Data Exchange Framework for Connected Vehicle Technology Supported Dynamic Transit Operations" (2015). All Theses. 2250.