What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Machine learning
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 most cited work include:
- Method of providing travel time (432 citations)
- MODELING DYNAMIC TRANSPORTATION NETWORKS (238 citations)
- A hybrid deep learning based traffic flow prediction method and its understanding (208 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Transport engineering (19.80%)
- Traffic flow (20.05%)
- Simulation (17.51%)
What were the highlights of his more recent work (between 2018-2021)?
- Traffic flow (20.05%)
- Real-time computing (14.21%)
- Artificial intelligence (12.18%)
In recent papers he was focusing on the following fields of study:
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.
Between 2018 and 2021, his most popular works were:
- Day-ahead traffic flow forecasting based on a deep belief network optimized by the multi-objective particle swarm algorithm (60 citations)
- Day-ahead traffic flow forecasting based on a deep belief network optimized by the multi-objective particle swarm algorithm (60 citations)
- Missing Value Imputation for Traffic-Related Time Series Data Based on a Multi-View Learning Method (46 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
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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