| Autor: |
Mohammed Naeem Qureshi, Nor Azlinah Md. Lazam |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Journal of King Saud University: Science, Vol 36, Iss 3, Pp 103107- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1018-3647 |
| DOI: |
10.1016/j.jksus.2024.103107 |
| Popis: |
Ride-hailing application price decisions are frequently considered biased and therefore are receiving more attention. Electronic (e)-hailing providers use the machine learning reinforcement model (RL) to build their dynamic pricing (DP) strategy to charge consumers. Nevertheless, the associated pricing issues in e-hailing potentially jeopardise this flourishing industry with a more significant long-term effect if left unresolved. Upon increased demand, the DP strategies assist the e-hailing systems with price surging, the unreasonable surging of price is unexpected by e-hailing users and deters them from using e-hailing services. A drawback of the existing RL DP algorithm is that does not consider surrounding factors before surging the price. Hence, this study aimed to address the underlying pricing issues through a hybrid pricing model using classification and regression tree (CART) supervised learning. A hybrid pricing algorithm was developed to demonstrate the enhanced model has edge over the existing model. The current e-hailing RL based algorithm was compared against the SL’s CART enhanced pricing algorithm with cross-validation using centrality analysis results. The test results shows that the hybrid pricing algorithm could address DP pricing issues by optimising e-hailing prices to provide its consumers with impartial pricing and remuneration. The proposed model can be a good theoretical reference for further studies which can also be applied to other industries such as Airlines, Tourism where DP is in used. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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