Efficient macroscopic urban traffic models for reducing congestion : A PDDL+ planning approach
- Submitting institution
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The University of Huddersfield
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 24
- Type
- E - Conference contribution
- DOI
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- Title of conference / published proceedings
- 30th AAAI Conference on Artificial Intelligence, AAAI 2016
- First page
- 3188
- Volume
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- Issue
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- ISSN
- -
- Open access status
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- Month of publication
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- Year of publication
- 2016
- URL
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- Supplementary information
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- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
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- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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4
- Research group(s)
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- Citation count
- 7
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This AAAI paper started the strand of research (with subsequent papers e.g. non-submitted IJCAI paper https://doi.org/10.24963/ijcai.2017/776 ) of using AI planning techniques for urban traffic control (UTC), utilising this paper’s PDDL+ flow model technique. A university joint-venture (http://www.simplifaisystems.com ) is now working on commercial solutions based on the results of this paper in real-world scenarios. Vallati obtained EPSRC funds via CP Catapult to explore the use of this approach for Connected and Autonomous Vehicles https://cp.catapult.org.uk/project/route-planning-for-autonomous-vehicles/. Further, Vallati has been awarded a UKRI Future Leaders Fellowship to extend the proposed UTC approach to work on an autonomic framework (https://www.gov.uk/government/news/uks-most-promising-scientists-backed-by-over-100-million-government-investment-to-bring-pioneering-ideas-to-market ).
- Author contribution statement
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- Non-English
- No
- English abstract
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