Reinforcement Learning Based Advertising Strategy Using Crowdsensing Vehicular Data
- Submitting institution
-
Liverpool John Moores University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2001
- Type
- D - Journal article
- DOI
-
10.1109/TITS.2020.2991029
- Title of journal
- IEEE Transactions on Intelligent Transportation Systems
- Article number
- -
- First page
- 1
- Volume
- 0
- Issue
- -
- ISSN
- 1524-9050
- Open access status
- Compliant
- Month of publication
- May
- Year of publication
- 2020
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
4
- Research group(s)
-
-
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work was produced as an output of a collaboration with researchers in China and the UK, supported by six grants such as 61772230 and 61972450 from the National Natural Science Foundations of China. It proposes a roadside digital billboard advertising strategy based on the vehicular data collected and processed by crowdsensing and reinforcement learning techniques. Extensive experiments were conducted to demonstrate that the proposed strategy outperforms related work, so it increases the effectiveness and thus profitability of the advertising.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
