His primary scientific interests are in Mathematical optimization, Intelligent transportation system, Simulation, Traffic flow and Transport engineering. The concepts of his Mathematical optimization study are interwoven with issues in Link and Nonlinear programming. His studies in Intelligent transportation system integrate themes in fields like Traffic generation model and Data mining.
His biological study deals with issues like Dynamic programming, which deal with fields such as Poisson distribution and Stochastic programming. Within one scientific family, he focuses on topics pertaining to Artificial neural network under Traffic flow, and may sometimes address concerns connected to Particle swarm optimization, Benchmark and Stability. The Transport engineering study combines topics in areas such as Real-time Control System and Network model.
His primary areas of investigation include Transport engineering, Traffic flow, Simulation, Real-time computing and Data mining. His Transport engineering study combines topics in areas such as Data collection and Flow network. The various areas that Bin Ran examines in his Traffic flow study include Traffic generation model, Control theory and Traffic congestion.
His Simulation study combines topics from a wide range of disciplines, such as Global Positioning System, Mathematical optimization, Traffic simulation and Trajectory. His primary area of study in Mathematical optimization is in the field of Optimal control. His Data mining research integrates issues from Intelligent transportation system, Missing data, Imputation and Artificial neural network, Artificial intelligence.
His scientific interests lie mostly in Traffic flow, Real-time computing, Artificial intelligence, Control theory and Transport engineering. Bin Ran combines subjects such as Intelligent transportation system, Data mining, Stability, Stability and Benchmark with his study of Traffic flow. His study explores the link between Real-time computing and topics such as Intelligent driver model that cross with problems in Linear stability theory.
His Artificial intelligence research includes themes of Machine learning and Travel time. His work in Control theory addresses issues such as Traffic congestion, which are connected to fields such as Transformation. Bin Ran has researched Transport engineering in several fields, including Mobile phone and Big data.
Traffic flow, Data mining, Control, Artificial intelligence and Stability are his primary areas of study. His work carried out in the field of Traffic flow brings together such families of science as Cooperative Adaptive Cruise Control, Stability, Stability conditions, Diagram and Statistical dispersion. His Data mining research is multidisciplinary, relying on both Intelligent transportation system, Cellular network, Random forest and Benchmark.
In the subject of general Artificial intelligence, his work in Deep learning is often linked to Full coverage, thereby combining diverse domains of study. His Deep learning research incorporates elements of Transport engineering, Imbalanced data and Pattern recognition. His Stability research is multidisciplinary, incorporating perspectives in Stability charts, Transfer function, Distributed computing and Optimal control.
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Dynamic Urban Transportation Network Models: Theory and Implications for Intelligent Vehicle-Highway Systems
Bin Ran;David E. Boyce.
Method of providing travel time
A hybrid deep learning based traffic flow prediction method and its understanding
Yuankai Wu;Huachun Tan;Lingqiao Qin;Bin Ran.
Transportation Research Part C-emerging Technologies (2018)
MODELING DYNAMIC TRANSPORTATION NETWORKS
Bin Ran;David Boyce.
A new class of instantaneous dynamic user-optimal traffic assignment models
Bin Ran;David E. Boyce;Larry J. LeBlanc.
Operations Research (1993)
Modeling Dynamic Transportation Networks: An Intelligent Transportation System Oriented Approach
Bin Ran;David E. Boyce.
Central processing and combined central and local processing of personalized real-time traveler information over internet/intranet
Bin Ran;Jing Li.
Energy-efficient metro train rescheduling with uncertain time-variant passenger demands: An approximate dynamic programming approach
Jiateng Yin;Tao Tang;Lixing Yang;Ziyou Gao.
Transportation Research Part B-methodological (2016)
Dynamic passenger demand oriented metro train scheduling with energy-efficiency and waiting time minimization: Mixed-integer linear programming approaches
Jiateng Yin;Lixing Yang;Tao Tang;Ziyou Gao.
Transportation Research Part B-methodological (2017)
Use of Local Linear Regression Model for Short-Term Traffic Forecasting
Hongyu Sun;Henry X. Liu;Heng Xiao;Rachel R. He.
Transportation Research Record (2003)
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