What is he best known for?
The fields of study he is best known for:
- Statistics
- Transport engineering
- Artificial intelligence
Matthew G. Karlaftis focuses on Transport engineering, Operations research, Econometrics, Intelligent transportation system and Artificial neural network.
His work carried out in the field of Transport engineering brings together such families of science as Algorithm and Regression.
His biological study spans a wide range of topics, including Transit and Fleet management.
His research integrates issues of Statistics, Bayesian probability, Statistical model and Mixed logit in his study of Econometrics.
His study in Intelligent transportation system is interdisciplinary in nature, drawing from both Field, Univariate and Traffic flow.
In his study, Traffic volume is inextricably linked to Data mining, which falls within the broad field of Artificial neural network.
His most cited work include:
- Statistical and econometric methods for transportation data analysis (1486 citations)
- Short-term traffic forecasting: Where we are and where we’re going (538 citations)
- Statistical methods versus neural networks in transportation research: Differences, similarities and some insights (470 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Transport engineering, Public transport, Operations research, Econometrics and Traffic flow.
He works mostly in the field of Transport engineering, limiting it down to concerns involving Genetic algorithm and, occasionally, Urban transportation and Emergency management.
His study in the field of Mode choice also crosses realms of Metropolitan area.
His Operations research study which covers Transit that intersects with Data envelopment analysis.
His Econometrics research is multidisciplinary, relying on both Regression analysis, Statistics and Time series.
His Traffic flow study deals with Data mining intersecting with Artificial neural network.
He most often published in these fields:
- Transport engineering (38.84%)
- Public transport (20.54%)
- Operations research (19.20%)
What were the highlights of his more recent work (between 2012-2020)?
- Transport engineering (38.84%)
- Public transport (20.54%)
- Artificial intelligence (6.70%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Transport engineering, Public transport, Artificial intelligence, Econometrics and Operations research.
His Transport engineering study combines topics in areas such as Network performance, Data envelopment analysis, Upstream and Flow network.
His work on Urban transit and Mode choice as part of general Public transport study is frequently linked to Athens greece, bridging the gap between disciplines.
His work is dedicated to discovering how Artificial intelligence, Machine learning are connected with Autoregressive model, Statistical inference and Bayesian probability and other disciplines.
His study in the field of Cointegration is also linked to topics like Modal.
His Operations research research is multidisciplinary, incorporating elements of Genetic algorithm, Service and Transit, Transit system.
Between 2012 and 2020, his most popular works were:
- Short-term traffic forecasting: Where we are and where we’re going (538 citations)
- A Real-Time Parking Prediction System for Smart Cities (93 citations)
- Sustainable urban transit network design (55 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Transport engineering
- Artificial intelligence
Matthew G. Karlaftis spends much of his time researching Transport engineering, Artificial neural network, Operations research, Artificial intelligence and Intelligent transportation system.
His Transport engineering study frequently intersects with other fields, such as Upstream.
In his works, he conducts interdisciplinary research on Operations research and Vehicle routing problem.
He interconnects Machine learning, Autoregressive model and Search algorithm in the investigation of issues within Artificial intelligence.
His research investigates the connection with Intelligent transportation system and areas like Time series which intersect with concerns in Univariate.
The various areas that Matthew G. Karlaftis examines in his Univariate study include Mathematical model, Computational intelligence and Data science.
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