Amrit Agarwal,,
Aryan Nama,,
Adit Jain,,
Divyansh Agarwal,,
Arpit Gupta,,
Neetu Joshi,
- Student, Department of Computer Science Engineering, Poornima College of Engineering, Poornima College of Engineering, Jaipur, Rajasthan, India
- Student, Department of Computer Science Engineering, Poornima College of Engineering, Poornima College of Engineering, Jaipur, Rajasthan, India
- Student, Department of Electronics and Telecommunication Poornima College of Engineering, Jaipur, Rajasthan, India
- Student, Department of Electronics and Telecommunication, Poornima College of Engineering, Jaipur, Rajasthan, India
- Student, Department of Electronics and Telecommunication, Poornima College of Engineering, Jaipur, Rajasthan, India
- Student, Department of Electronics and Telecommunication, Poornima College of Engineering, Jaipur, Rajasthan, India
Abstract
This article provides an in-depth analysis of Uber’s data analytics, clarifying its crucial role in revolutionizing the transportation industry. It provides vital insights for stakeholders by carefully examining rider patterns, driving behaviors, and market dynamics. This study provides a thorough knowledge of Uber’s impact on urban transportation and socio-economic landscapes by combining existing research and proposing fresh approaches. Uber is a digital company that has made multiple attempts to leverage machine learning and data science methods to enhance its offerings. However, most of these have focused on the technical aspects of the service, such as figuring out the consumer surplus. The examination of rider behavior, driver dynamics, market developments, and the wider implications for the sustainability of transportation are among the key subjects. Examining user reviews that have been published online is an effective way to analyze user experience. Enhancing products or services may begin with the analysis of user-oriented datasets. An easily accessible form of user-oriented dataset that can represent a variety of viewpoints and feelings about a good or service is an online review. This study provides a significant resource for scholars, regulators, and industry experts who aim to optimize ride-sharing systems for increased efficiency and societal benefit. It addresses methodological challenges and suggests future research options.
Keywords: Uber, data analytics, transportation, ridesharing, urban mobility, sustainability.
[This article belongs to Current Trends in Signal Processing(ctsp)]
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Current Trends in Signal Processing
| Volume | 14 |
| Issue | 01 |
| Received | May 5, 2024 |
| Accepted | May 14, 2024 |
| Published | August 16, 2024 |