What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Machine learning
His main research concerns Simulation, Statistics, Transport engineering, Collision and Mathematical optimization.
His Simulation research incorporates themes from Traffic flow, Passenger information, Geographic information system, Geocoding and Traffic congestion.
His work in Statistics addresses subjects such as Data mining, which are connected to disciplines such as Crash data, Kriging method, Kriging and Kernel density estimation.
His Transport engineering study integrates concerns from other disciplines, such as Visibility and Road surface.
His studies in Collision integrate themes in fields like Posterior probability, Crash severity and Bayes' theorem.
In his study, Bayesian probability is inextricably linked to Econometrics, which falls within the broad field of Poisson distribution.
His most cited work include:
- Expected shortest paths in dynamic and stochastic traffic networks (249 citations)
- Heuristic shortest path algorithms for transportation applications: state of the art (245 citations)
- Real-Time Optimization Model for Dynamic Scheduling of Transit Operations (140 citations)
What are the main themes of his work throughout his whole career to date?
Liping Fu mainly investigates Transport engineering, Simulation, Road surface, Snow and Winter maintenance.
His studies deal with areas such as Collision and Operations research as well as Transport engineering.
His Collision research includes elements of Statistics and Ordered logit.
His research on Simulation also deals with topics like
- Real-time computing which intersects with area such as Artificial neural network,
- Bayesian probability which intersects with area such as Poisson distribution.
Liping Fu has included themes like Visibility, Winter storm, Negative binomial distribution, Road Weather Information System and Condition monitoring in his Road surface study.
His work deals with themes such as Level of service and Mathematical model, which intersect with Winter maintenance.
He most often published in these fields:
- Transport engineering (35.17%)
- Simulation (17.37%)
- Road surface (18.64%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial neural network (11.02%)
- Artificial intelligence (10.59%)
- Data mining (15.68%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial neural network, Artificial intelligence, Data mining, Deep learning and Transport engineering.
His work in the fields of Deep belief network and Overfitting overlaps with other areas such as Inverse, Filter design and Inverse problem.
His research investigates the link between Artificial intelligence and topics such as Machine learning that cross with problems in Maintenance engineering, Level of service and Road surface.
His research integrates issues of Random forest, Linear regression, Reliability and Support vector machine in his study of Data mining.
His work in Random forest covers topics such as Interval which are related to areas like Simulation.
His Transport engineering research is multidisciplinary, relying on both Control system and Winter weather.
Between 2018 and 2021, his most popular works were:
- A hybrid Bayesian network model for predicting delays in train operations (23 citations)
- A hybrid Bayesian network model for predicting delays in train operations (23 citations)
- A deep learning approach for multi-attribute data: A study of train delay prediction in railway systems (11 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
Liping Fu spends much of his time researching Data mining, Support vector machine, Artificial neural network, Bayesian network and Measure.
A majority of his Data mining research is a blend of other scientific areas, such as Hybrid model and Train.
The concepts of his Artificial neural network study are interwoven with issues in Dynamical systems theory, State, Deep learning and Convolutional neural network, Pattern recognition.
His Bayesian network research includes themes of Mean absolute error and Domain knowledge.
His biological study spans a wide range of topics, including Random forest, Reliability and Linear regression.
Much of his study explores Artificial intelligence relationship to Mean squared error.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
