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Civil, Engineering, School of EngineeringOffice: EME3275
Graduate student supervisor
Infrastructure Digitization; Virtual Transportation Infrastructure Management; Automated LiDAR Data Processing
Road Safety Analytics; Roadway Design; Smart Cities; Resilient Transportation Infrastructure
Courses & Teaching
Dr. Suliman A. Gargoum is an Assistant Professor of Civil Engineering at the School of Engineering, the University of British Columbia (UBC). His research focuses on the use of smart sensing technology for advanced road safety analytics and informed design and management of transportation infrastructure.
Dr. Gargoum received his PhD and MSc in Transportation Engineering from the University of Alberta. Upon completing his PhD, he joined Nektar Inc. as Chief Research Officer where he gained significant industrial experience and worked on translating his doctoral research into industrial applications to help support transportation infrastructure projects.
Dr. Gargoum’s research has resulted in several peer-reviewed research papers, which have been published in top tier transportation journals and presented at several national and international conferences. He has also received multiple best paper and best presentation awards including accolades from the Transportation Association of Canada (TAC), the Transportation Research Board (TRB), and the Canadian Society of Civil Engineers (CSCE).
PhD – (Transportation Engineering), University of Alberta, Canada
MSc – (Transportation Engineering), University of Alberta, Canada
BSc with honours – (Civil and Environmental Engineering), UAE University, United Arab Emirates
Research Interests & Projects
Dr. Gargoum’s research interests lie in a range of topics related to intelligent transportation infrastructure management and advanced road safety analytics. He is particularly interested in the use of machine learning techniques, deep neural networks, and stochastic simulation techniques to help design safer and more resilient transportation infrastructure.
His work also focuses on developing decision support tools that help transportation agencies utilize sensor technology including mobile LiDAR data and imagery for informed management of transportation infrastructure.
Dr. Gargoum’s work has involved significant collaboration with several municipal, provincial and federal entities including the National Research Council (NRC) of Canada, Alberta Innovates, Alberta Transportation, and the City of Edmonton.
The following list includes a selection of themes that Dr. Gargoum focuses on in his research
- Theme 1 – Employing machine learning techniques and deep neural networks for virtual extraction of information from point cloud and image data for road safety analytics and infrastructure management.
- Theme 2 – The use of reliability theory and statistical simulation to integrate resiliency into the design and management of transportation infrastructure of the future.
- Theme 3 – Using geospatial data analytics for network-level modelling of transportation infrastructure resiliency, including vulnerability and risk assessment.
- Theme 4 – Time series analysis and structural equation modelling of the speed and safety for the adoption of improved speed management strategies.
Note to Potential Applicants:
Dr. Gargoum is currently accepting applications for Master’s and Doctoral students interested in joining his research group starting Jan 2022, May 2022, and Sept 2022. Candidates are expected to have interest/experience in one or two of the following areas: (i) transportation and highway engineering, (ii) GIS and geospatial data analytics, (iii) statistics, (iv) machine learning, and (v) deep learning.
Preference will be given to applicants with strong experience in Python and the use of machine learning libraries (tensorflow/pytorch). Prior research experience (publications) is considered an asset.
Interested applicants should send an email to Dr. Gargoum under the title “Prospective Graduate Student” while attaching the following:
- A cover letter including a short summary of why you are interested in joining Dr. Gargoum’s group and any relevant experience you might have.
- An updated CV and copies of transcripts
- One or two samples of previous written work (eg: publications (if any), project reports, literature reviews, essays…etc).
- Any other relevant information (sample publications, link to github page…etc)
Selected Publications & Presentations
Selected Publications (Full list: https://scholar.google.com/citations?user=YWgWTMcAAAAJ&hl=en)
Gargoum, S. A., & Karsten, L. (2021). Virtual assessment of sight distance limitations using LiDAR technology: Automated obstruction detection and classification. Automation in Construction, 125, 103579.
Gargoum, S., and Gargoum, A. (2021). Limiting Mobility During COVID-19 When and to What Level? An International Comparative Study using Change Point Analysis. Journal of Transport and Health.
Gargoum, S. and El-Basyouny, K. (2020) “Analysing the Ability of Crash Prone Highways to Handle Stochastically Modelled Driver Demand for Stopping Sight Distance” Accident Analysis and Prevention.
Gargoum, S. and El-Basyouny, K. (2019) “A Literature Synthesis of LiDAR Applications in
Transportation: Feature Extraction and Geometric Assessments of Highways” Journal of GIScience Remote Sensing.
Gargoum, S. and El-Basyouny, K. (2019) “Effects of LiDAR Point Density on Extraction of Traffic Signs: A Sensitivity Study” Transportation Research Record: Journal of the Transportation Research Board.
Gargoum, S., El-Basyouny, K., Karsten, L., and Koch, J. (2018) “Automated Assessment of Vertical Clearance on Highways scanned Using Mobile LiDAR Technology” Automation in Construction.
Gargoum, S., El-Basyouny, K., Froese, K., and Gadowski, A. (2018) “A Fully Automated Approach to Extract and Assess Road Cross Sections from Mobile LiDAR Data” IEEE Transactions on Intelligent Transportation Systems.
Gargoum, S., Koch, J., and El-Basyouny, K. (2018) “A Voxel-Based Method for Automated Detection and Mapping of Light Poles on Rural Highways using LiDAR data” Transportation Research Record: Journal of the Transportation Research Board.
Gargoum, S., Tawfeek, M. H., El-Basyouny, K., and Koch, J. (2018) “Available Sight Distance on Existing Highways: Meeting Stopping Sight Distance Requirements of an Ageing Population” Accident Analysis and Prevention.
Gargoum, S., El-Basyouny, K., and Kim, A. (2016) “Towards Setting Credible Speed Limits: Identifying Factors that Affect Driver Compliance on Urban Roads” Accident Analysis and Prevention.
Selected Grants & Awards
- Utilizing Deep Learning for Fully Automated Feature Extraction from Mobile LiDAR Scans of Roadways – National Research Council of Canada, Ottawa, ON.
- Intelligent Pothole Detection and Assessment on Urban Roads in the City of Edmonton using Mobile LiDAR Technology – City of Edmonton, Edmonton, AB.
Scholarships and Awards
- 2021 Canadian ITS R&D/Innovation Award, awarded for work conducted on “Digital Transformation of Transportation Infrastructure Using LiDAR and AI”. (2021)
- 2020 NACITE’s NextGen Star by the Canadian Institute of Transportation Engineer’s Northern Alberta Chapter (2020)
- 2020 Canadian ITS R&D/Innovation Award, awarded for work conducted on “Information Fusion in Detection, Recognition, and Classification of Road Infrastructure from Remote Sensing Data” in 2018. (2020)
- Best Presentation Award for Doctoral Research in Transportation Safety at the 98th Transportation Research Board Annual Meeting (2019) – Sponsored by technical committee on Safety Data, Analysis and Evaluation (ANB20) and Statistical Methods (ABJ80)
- Transportation Association of Canada Best Paper Awards (3) – (2014, 2017)
- Alberta Innovates – Technology Futures (AITF) Scholarship (2017)
- Queen Elizabeth II Graduate Scholarship (2017)
- Guest Editor for the Special Issue on Designing and Managing the Next Generation of Transportation Infrastructure for Remote Sensing and Infrastructure Journals published by MDPI
- Member of the Institute of Transportation Engineers (ITE)
- Member of the Consulting Engineers of Alberta (CEA).
- Member of the Intelligent Transportation Systems Canada (ITS Canada)