Short-Term Traffic Flow Forecasting A wide and deep approach with periodic feature selection
Martin Esugo, Qian Lu, Olivier Haas. "Short-Term Traffic Flow Forecasting A wide and deep approach with periodic feature selection." TechRxiv. May 17, 2022
CV
Education
- Ph.D. in Transportation Engineering, Coventry University, 2023.
- M.Sc. in Control Engineering, Coventry University, 2017.
- B.Sc. in Electrical/Electronics Engineering, University of Lagos, 2015.
Experience
- Research Fellow || Birmingham City University || Apr. 2024 - Current
- Provide AI consultancy and assistance to MedTech companies in the West Midlands (United Kingdom).
- Postgraduate Researcher || Coventry University || Sep. 2019 - Sep. 2023
- Developed advanced deep-learning-based models, data preprocessing techniques and evaluation methods for traffic flow forecasting.
- Python libraries used: NumPy, Pandas, Matplotlib, TensorFlow/Keras.
- Research Assistant || Coventry University || Jul. 2022 - Dec. 2022
- Annotated road-marking images and managed the preprocessing and augmentation of 150,000 images.
- Trained a highly effective road-marking detection model in Detectron2.
- Research Assistant || Coventry University || Nov. 2017 - Nov. 2018
- Developed advanced traffic simulation models for the iVMS (Intelligent Variable Message Systems) and UKCITE (UK Connected Intelligent Transport Environment) projects.
- Simulated sophisticated driver assistance features in VISSIM and AIMSUN.
Skills
- Programming Languages: Python, SQL.
- Programming Toolset: Tensorflow/Keras, PyTorch, NumPy, Pandas, Matplotlib, OpenCV, LangChain.
- Technology Stack: Deep Learning, Natural Language Processing, Time Series Forecasting, Computer Vision.
- Platform Proficiency: Amazon SageMaker and Bedrock, Flowise, LangFlow.
Publications
A VISSIM based ADAS simulation platform to complement the UKCITE real world connected vehicle test environment
Esugo, Martin & Haas, Olivier & Agbaje, Oluwaleke & Antoine, Stephan & Matheo, Girbal. (2018). "A VISSIM based ADAS simulation platform to complement the UKCITE real world connected vehicle test environment." Conference: 25th ITS World Congress