Decision support system (DSS) automation for integrated corridor management (ICM)
- Jul 15, 2021
- Focus Area
- Transportation Systems Management & Operations
- Submitting On Behalf Of
- TRB Committee on Freeway Operations
- Urgency
- Critical - High Priority
- Cost
- $250,000 - 499,000
- Timeframe
- 1 - 2 years
- Type of Research
- Full Research Project
- Date Posted
- Jul 15, 2021
- Status
- Not Funded
Research Description
There is a need to improving machine learning capacity in order to achieving full or nearly full DSS automation. Decision Support System (DSS) has been approved to be one of effective tools in supporting integrated corridor management (ICM). The traditional DSS systems in supporting ICM commonly used traditional technologies such as data fusion and decision tree. The advanced development of machine learning (ML) and deep learning base Artificial Intelligence (AI) methodologies and algorithms provide new improving spaces of DSS enhancements.
Additional Supporting Information
To achieve robust ML and AI capabilities, there are many issues desired in-deep researches, such as • Sustainability of ML algorithms. – Most of the existing ML algorithms might be only working for certain dataset and will low it performance in dealing with living data environments. Decent questions need to answer such as, how long the models need to be re-trained, etc. • Distributed and collaborative AI capability. – ICM is normally a complex system with widely deployed device and data sources integration. • Deployment efficiency. – How to rapidly deploy matured ML technologies and what’s the potential standards and guidance for data preparing and models training? • How to incorporation of ML models with living simulation systems in supporting ICM functions.
- Submitted By
- Jim Katsafanas
- Michael Baker International
- 412-269-4635
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