Comparative Analysis of Ride-On-Demand Services for Fair Price Detection Using Machine Learning Article Swipe
Metta Dhana Lakshmi
,
J. Revathi
,
C H Nikhitha Sravani
,
Maddila Adarsa Suhas
,
Balagam Umesh
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.22214/ijraset.2024.60337
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.22214/ijraset.2024.60337
The project titled "Comparative Analysis of Ride-On-Demand Services for Fair Price Detection Using Machine Learning" aims to investigate and evaluate the methodologies employed by different ride-on-demand platforms to determine equitable pricing through the application of machine learning algorithms. The primary focus of this research is to assess the effectiveness, transparency, and adaptability of pricing mechanisms in the context of dynamic factors such as geographical location, time of day, cab type, source, destination and weather conditions. The project involves a comprehensive comparative analysis of various ride-on-demand services, exploring the diversity of machine learning models utilized for fair price detection. The study will delve into the accuracy of price predictions, considering real-time demand fluctuations and the adaptability of algorithms to dynamic operational environments. Transparency in pricing decisions will be a key parameter for evaluation, as clear and understandable explanations are crucial for establishing user trust. The research methodology includes data collection from multiple ride-on-demand platforms like Uber, Ola, Rapido and Indrive, analysis of pricing algorithms, and the development of performance metrics to assess the fairness and efficacy of each service. The project aims to provide insights into best practices for implementing machine learning in ride-on-demand services, with the ultimate main goal of enhancing user experience and fostering trust within the user community. The findings of this comparative analysis will contribute valuable knowledge to the field of transportation technology and assist in shaping future advancements in fair price detection mechanisms
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Metadata
- Type
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- Language
- en
- Landing Page
- http://doi.org/10.22214/ijraset.2024.60337
- https://doi.org/10.22214/ijraset.2024.60337
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All OpenAlex metadata
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https://openalex.org/W4394946172Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.22214/ijraset.2024.60337Digital Object Identifier
- Title
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Comparative Analysis of Ride-On-Demand Services for Fair Price Detection Using Machine LearningWork title
- Type
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articleOpenAlex work type
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-04-18Full publication date if available
- Authors
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Metta Dhana Lakshmi, J. Revathi, C H Nikhitha Sravani, Maddila Adarsa Suhas, Balagam UmeshList of authors in order
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https://doi.org/10.22214/ijraset.2024.60337Publisher landing page
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https://doi.org/10.22214/ijraset.2024.60337Direct link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://doi.org/10.22214/ijraset.2024.60337Direct OA link when available
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Computer science, Machine learning, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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