Time-Series Analysis of Uber Ride Trends

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Year : August 16, 2024 at 3:35 pm | [if 1553 equals=””] Volume :14 [else] Volume :14[/if 1553] | [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] : 01 | Page : 34-38

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Amrit Agarwal,, Aryan Nama,, Adit Jain,, Divyansh Agarwal,, Arpit Gupta,, Neetu Joshi,

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  1. Student, Department of Computer Science Engineering, Poornima College of Engineering,, Student, Department of Computer Science Engineering, Poornima College of Engineering,, Student, Department of Electronics and Telecommunication, Student, Department of Electronics and Telecommunication,, Student, Department of Electronics and Telecommunication,, Student, Department of Electronics and Telecommunication, Poornima College of Engineering,, Poornima College of Engineering,, Poornima College of Engineering,, Poornima College of Engineering,, Poornima College of Engineering,, Poornima College of Engineering, Jaipur, Rajasthan,, Jaipur, Rajasthan,, Jaipur, Rajasthan,, Jaipur, Rajasthan,, Jaipur, Rajasthan,, Jaipur, Rajasthan, India, India, India, India, India, India

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Abstract

nThis 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.

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Keywords: Uber, data analytics, transportation, ridesharing, urban mobility, sustainability.

n[if 424 equals=”Regular Issue”][This article belongs to Current Trends in Signal Processing(ctsp)]

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[/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue under section in Current Trends in Signal Processing(ctsp)][/if 424][if 424 equals=”Conference”]This article belongs to Conference [/if 424]

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How to cite this article: Amrit Agarwal,, Aryan Nama,, Adit Jain,, Divyansh Agarwal,, Arpit Gupta,, Neetu Joshi. Time-Series Analysis of Uber Ride Trends. Current Trends in Signal Processing. August 16, 2024; 14(01):34-38.

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How to cite this URL: Amrit Agarwal,, Aryan Nama,, Adit Jain,, Divyansh Agarwal,, Arpit Gupta,, Neetu Joshi. Time-Series Analysis of Uber Ride Trends. Current Trends in Signal Processing. August 16, 2024; 14(01):34-38. Available from: https://journals.stmjournals.com/ctsp/article=August 16, 2024/view=0

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References

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  1. Kooti F, Grbovic M, Aiello LM, Djuric N, Radosavljevic V, Lerman K. Analyzing Uber’s ride-sharing economy. InProceedings of the 26th international conference on world wide web companion 2017 Apr 3 (pp. 574-582).
  2. Yan C, Zhu H, Korolko N, Woodard D. Dynamic pricing and matching in ride‐hailing platforms. Naval Research Logistics (NRL). 2020 Dec;67(8):705-24.
  3. Carbonneau R, Laframboise K, Vahidov R. Application of machine learning techniques for supply chain demand forecasting. European journal of operational research. 2008 Feb 1;184(3):1140-54.
  4. Zhu L, Laptev N. Deep and confident prediction for time series at uber. In2017 IEEE International Conference on Data Mining Workshops (ICDMW) 2017 Nov 18 (pp. 103-110).
  5. Qin ZT, Zhu H, Ye J. Reinforcement learning for ridesharing: An extended survey. Transportation Research Part C: Emerging Technologies. 2022 Nov 1;144:103852.
  6. Shokoohyar S, Sobhani A, Sobhani A. Impacts of trip characteristics and weather condition on ride-sourcing network: Evidence from Uber and Lyft. Research in transportation economics. 2020 May 1;80:100820.
  7. Rosenblat A, Hwang T. Regional diversity in autonomy and work: A case study from Uber and Lyft drivers. Intelligence and Autonomy. 2016 Oct;1:15.
  8. Guo Y, Zhang Y, Boulaksil Y, Qian Y, Allaoui H. Modelling and analysis of online ride-sharing platforms–A sustainability perspective. European Journal of Operational Research. 2023 Jan 16;304(2):577-95.
  9. Mitropoulos L, Kortsari A, Ayfantopoulou G. A systematic literature review of ride-sharing platforms, user factors and barriers. European Transport Research Review. 2021 Dec;13:1-22.
  10. Fatoki Y, Fasuyi PO, Olorunnipa TA. An Examination of the GIG Economy: A Case Study of Uber. Journal of Commercial and Property Law. 2024 Jun 26;11(3):167-79.
  11. Rajesh R. Study Of Customer Experience And Uses Of Uber Cab Services In Mumbai. International Journal of Application or Innovation in Engineering & Management. 2021;10(6):050-61.
  12. Kim JH, Nan D, Kim Y, Park MH. Computing the user experience via big data analysis: a case of Uber services. CMC-Computers Materials & Continua. 2021 Jan 1;67(3):2819-29.
  13. Williamson OE. Transforming merger policy: the pound of new perspectives. The American
  14. Deaton A. Demand analysis. Handbook of econometrics. 1986 Jan 1;3:1767-839.
  15. Baker JB, Bresnahan TF. Estimating the residual demand curve facing a single firm,International Journal of Industrial Organization. 1988 Jan 1;6(3):283-300.

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[if 424 not_equal=””]Regular Issue[else]Published[/if 424] Subscription Review Article

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Volume 14
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 01
Received May 5, 2024
Accepted May 14, 2024
Published August 16, 2024

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