IEEE Transactions on Intelligent Transportation Systems · 2011 · 1.8K citations · 120 references
EngineeringComputer ScienceSystems EngineeringTransportTransportationTransportation EngineeringIntelligent Transportation SystemsTransport SystemsTransportation Decision SupportTransportation SystemsIntelligent Transportation Systems Decision SupportTransportation System OptimizationTransportation Systems Integration
For the last two decades, intelligent transportation systems (ITS) have emerged as an efficient way of improving the performance of transportation systems, enhancing travel security, and providing more choices to travelers. A significant change in ITS in recent years is that much more data are collected from a variety of sources and can be processed into various forms for different stakeholders. The availability of a large amount of data can potentially lead to a revolution in ITS development, changing an ITS from a conventional technology-driven system into a more powerful multifunctional data-driven intelligent transportation system (D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ITS) : a system that is vision, multisource, and learning algorithm driven to optimize its performance. Furthermore, D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ITS is trending to become a privacy-aware people-centric more intelligent system. In this paper, we provide a survey on the development of D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ITS, discussing the functionality of its key components and some deployment issues associated with D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ITS Future research directions for the development of D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ITS is also presented.
120
Sinno Jialin Pan, Qiang Yang · IEEE Transactions on Knowledge and Data Engineering · 2009 · 23.2K citations
Nonlinear Dimensionality Reduction by Locally Linear Embedding
Sam T. Roweis, Lawrence K. Saul · Science · 2000 · 15K citations
Learning the parts of objects by non-negative matrix factorization
Daniel D. Lee, H. Sebastian Seung · Nature · 1999 · 14.1K citations
A Global Geometric Framework for Nonlinear Dimensionality Reduction
Joshua B. Tenenbaum, Vin de Silva, John Langford · Science · 2000 · 13.7K citations