Transportation agencies are turning to smarter ways with a variety of new technologies to improve travel experience and to manage the transportation system more efficiently. Over the last decade, new technologies and innovative transportation systems produce big data, which has tremendously enriched ways to monitor and manage the transportation systems. A challenging question is, how do we make the best use of the big data to design and operate our transportation system? In addition, those data sources are usually established by disparate public agencies and private sector. They rarely communicate with each other and as a result, each part of the transportation systems is individually operated and clearly, the entire transportation system is far from being socially optimal. Integrating and learning the big data are the keys to success of smarter transportation systems, which consist of the following three components. (1) Integration of various data sources: (2) Understanding of integrated data: and (3) Optimal decision making for systems management and for individual travelers. A mobility data analytics center is necessary to accommodate needs of data fusion and analytics. The ultimate objective of mobility data analytics center is to: (1) Provide archived and real-time traffic data of every element of multi-modal transportation systems; (2) Reveal the behavior information for both passenger transportation and freight transportation: (3) Serve as a key managerial instrument for legislators, transportation planners, researchers, and engineers: (4) Serve as a key information platform for individual travelers and transportation industries. This research serves as a first stage of developing the mobility data center. The project will focus on integrating and learning large-scale transportation data sets that are immediately available to collect and the most promising from public agencies and travelers. The City of Pittsburgh was chosen as a demonstration city. Pittsburgh has been successful in moving towards smart cities and smart transportation systems, with ample resources or us to collect and work with large-scale citywide data.
|firstname.lastname@example.org||Qian, Sean||Carnegie Mellon University||PI||Faculty - Researcher/Post-Doc|
|Final Report||208_-_Report_RTIS_Benedum.pdf||June 29, 2018, 5:09 a.m.|
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