Parallel Control and Management for Intelligent Transportation Systems: Concepts, Architectures, and Applications

By Fei-Yue Wang. IEEE transactions on intelligent transportation systems (Print), 2010

817 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/dbe217b10bdb05d0e6e218fb64519ea3df28d4ca

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Citations in the corpus

Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning (2014, background): “Long-term traffic flow prediction is as important as shortterm traffic flow prediction in a transportation system [33].”
Parallel Transportation Systems: Toward IoT-Enabled Smart Urban Traffic Control and Management (2020, background): “Finally, the actual and artiﬁcial systems are executed in an interactive parallel mode (P) [15].”
A Survey of Vehicle to Everything (V2X) Testing (2019, background): “Parallel testing can effectively reduce the cost of large-scale testing [105].”
Optimal Perimeter Control for Two Urban Regions With Macroscopic Fundamental Diagrams: A Model Predictive Approach (2013, background): “Moreover, networks with a small variance of link densities have a well-defined MFD, i.e., low scatter of flows for the same densities.”
Urban traffic signal control with connected and automated vehicles: A survey (2019, background): “Urban traffic control (UTC) systems have also been continuously updated and innovated to keep up with the increasing traffic demands (Wang 2010, Hamilton et al., 2013; Li and Wang, 2018).”
Capturing Car-Following Behaviors by Deep Learning (2018, unknown): “the near future [57]–[59].”
Stability and Scalability of Homogeneous Vehicular Platoon: Study on the Influence of Information Flow Topologies (2016, background): “P LATOONING of road vehicles provides a promising solution to several critical issues of today’s road transportation due to its potential to significantly increase highway capacity, enhance safety, and reduce fuel consumption, as well as CO2 emission [1]–[3].”
Data-Driven Intelligent Transportation Systems: A Survey (2011, background): “The recent development in parallel control and management for ITS [129], which explicitly incorporates engineering and social complexities into the modeling and decision making of a large-scale system, has also provided an ideal platform for deploying D(2)ITS.”
Travel time prediction with LSTM neural network (2016, background): “With real-time travel time, the intelligent transportation system (ITS) [1] can offer information services to decision makers for traffic control or guidance operations and to travelers for route selection and planning.”
Smart logistics based on the internet of things technology: an overview (2020, background): “A management information system is required to achieve information sharing and coordination in smart transportation and delivery (Wang 2010).”
Computational Intelligence in Urban Traffic Signal Control: A Survey (2012, methodology): “Network architecture of the aDAPTS [10].”
Towards blockchain-based intelligent transportation systems (2016, methodology): “Following this practice trend, the research focus of the ITS literature [ 2 (cid:0) 7 ] has also been witnessed to shift from transportation management and surveillance to vehicle-and vision-related topics in recent years.”
Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives (2023, background): “by a large number of implementations in practice [134], such as parallel intelligence [135], parallel control [136], [137], parallel management [138], parallel transportation [139], parallel driv-”