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Lei Fang - IEEE Xplore Author Profile

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Author details

Lei Fang

Also published under: L. Fang

Publications

3

Citations

374

Publications by Year

20062008

Co-Authors:

T. AbdelzaherQ. CaoT. DoanZ. HeT. He

Show All Co-Authors (13)

Lei Fang

Also published under: L. Fang

Affiliation

Software engineer, Microsoft Corporation, USA

Publication Topics

Sensor Networks,
Wireless Sensor Networks,
Assisted Living,
Motion Detection,
Power Management,
Sensor Devices,
Wireless Sensor,
Accelerometer,
Activity Patterns,
Actuator,
Aging In Place,
Baby Boomer Generation

Biography

Lei Fang is a software engineer at Microsoft Corporation. She received her Master's degree in computer science from the University of Virginia in 2006. Her research interests are secure routing in wireless sensor networks.(Based on document published on 25 July 2008).

Publications

3

Citations

374

Publications by Year

20062008

Co-Authors:

T. Abdelzaher
Q. Cao
T. Doan
Z. He
T. He

Show All Co-Authors (13)

Author's Published Works

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Showing 1-4 of 4 results

Conferences (3)

Journals (1)

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S. Sacone(4)
C. Pasquale(4)
K. Binjaku(3)
E. K. Meçe(3)
Kleona Binjaku(1)
Department of Computer Engineering, Polytechnic University of Tirana, Albania(3)
Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genova, Italy(3)
Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genova, Genoa, Italy(1)
Department of Computer Engineering, Polytechnic University of Tirana, Tiranë, Albania(1)
2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)(1)
2024 IEEE 20th International Conference on Automation Science and Engineering (CASE)(1)
2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)(1)
IEEE Open Journal of Intelligent Transportation Systems(1)
IEEE(4)
Bari, Italy(1)
Bilbao, Spain(1)
Edmonton, AB, Canada(1)
Traffic Flow(4)
Traffic Model(4)
Traffic Prediction(4)
Highway(3)
Long Short-term Memory Model(3)

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Freeway Traffic Modeling by Physics-Regularized Gaussian Processes

Kleona Binjaku;C. Pasquale;E. K. Meçe;S. Sacone

IEEE Open Journal of Intelligent Transportation Systems

Year: 2025 | Volume: 6 | Journal Article |

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Effective traffic management and control are essential for mitigating congestion and minimizing environmental impacts on road transportation systems. In this paper, we propose a novel approach for traffic modeling that integrates physics-based dynamics with machine learning techniques. Our method leverages Gaussian Processes (GPs) and a multi-class second-order discrete traffic model known as META...Show More

Freeway Traffic Modeling by Physics-Regularized Gaussian Processes

Kleona Binjaku;C. Pasquale;E. K. Meçe;S. Sacone

IEEE Open Journal of Intelligent Transportation Systems

Year: 2025 | Volume: 6 | Journal Article |

AI-Based Predictive Ramp-Metering Control for Freeway Traffic Systems

K. Binjaku;E. K. Meçe;C. Pasquale;S. Siri;S. Sacone

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)

Year: 2024 | Conference Paper |

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Traffic congestion in freeways poses significant challenges, impacting travel times and environmental sustainability. This paper proposes a novel approach to enhance ramp metering control using predictive traffic insights derived from physics-informed LSTM (Long Short-Term Memory) models. By integrating predictive capabilities with established control strategies like ALINEA, the method dynamically...Show More

AI-Based Predictive Ramp-Metering Control for Freeway Traffic Systems

K. Binjaku;E. K. Meçe;C. Pasquale;S. Siri;S. Sacone

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)

Year: 2024 | Conference Paper |

A Traffic Model Integrating Long Short-Term Memory Networks with Multi-class Macroscopic Equations

K. Binjaku;C. Pasquale;S. Sacone;E. K. Meçe

2024 IEEE 20th International Conference on Automation Science and Engineering (CASE)

Year: 2024 | Conference Paper |

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Accurate traffic state estimation is crucial for effective transportation management and urban planning. In recent years, deep learning techniques, particularly Long Short-Term Memory (LSTMs) networks, have shown promise in capturing the complex temporal dynamics of traffic systems. This paper proposes a novel approach by integrating LSTM networks with the multi-class METANET model, considering on...Show More

A Traffic Model Integrating Long Short-Term Memory Networks with Multi-class Macroscopic Equations

K. Binjaku;C. Pasquale;S. Sacone;E. K. Meçe

2024 IEEE 20th International Conference on Automation Science and Engineering (CASE)

Year: 2024 | Conference Paper |

Control Oriented Freeway Traffic Modelling by Physics-Regularized Machine Learning

K. Binjaku;E.K. Meçe;C. Pasquale;S. Sacone

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

Year: 2023 | Conference Paper |

Cited by: Papers (5)

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A modelling framework for freeway traffic systems specifically oriented to the definition of suitable control schemes is developed in this paper. The traffic system includes different vehicle classes (passengers and freight vehicles) whose behaviour is explicitly modelled. Vehicles can enter the free-way arriving from the mainstream or through on-ramps on which traffic volumes may be regulated wit...Show More

Control Oriented Freeway Traffic Modelling by Physics-Regularized Machine Learning

K. Binjaku;E.K. Meçe;C. Pasquale;S. Sacone

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

Year: 2023 | Conference Paper |

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