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International Journal of Science, Strategic Management and Technology

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ISSN: 3108-1762 (Online)
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DATA-DRIVEN TAXI FARE ANALYSIS USING MICROSOFT POWER BI: DESIGN, IMPLEMENTATION AND BUSINESS INSIGHTS

AUTHORS:
D Priti Patra
Mentor
Allupati Chakradhar Patro
Affiliation
Master of Computer Application GIFTAutonomous  BPUT University
CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
The transportation industry generates large volumes of operational data through daily taxi bookings, fare transactions, customer interactions, and service activities. Analyzing this data effectively is essential for improving business performance, customer satisfaction, and operational efficiency. Traditional reporting methods often fail to provide dynamic insights and require significant manual effort. Therefore, Business Intelligence tools have become increasingly important for transforming raw transportation data into meaningful information.

This paper presents a Taxi Fare Analysis Dashboard developed using Microsoft Power BI. The proposed system utilizes data cleaning, transformation, modeling, and visualization techniques to analyze taxi booking data and generate actionable business insights. The dashboard provides detailed analysis of booking trends, revenue generation, vehicle performance, cancellation patterns, payment methods, and customer ratings. Power Query and Data Analysis Expressions (DAX) are used to preprocess and analyze data efficiently. Interactive visualizations such as KPI cards, charts, graphs, and slicers enable users to explore business performance dynamically.

The developed system demonstrates how Business Intelligence technologies can support data-driven decision-making in transportation services. The results indicate that Power BI provides an effective platform for analyzing taxi operations and identifying opportunities for business improvement.
Keywords
Power BI Business Intelligence Data Analytics Taxi Fare Analysis Dashboard Development Data Visualization Transportation Analytics.
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Patra, D. P. (2026). Data-Driven Taxi Fare Analysis Using Microsoft Power BI: Design, Implementation And Business Insights. International Journal of Science, Strategic Management and Technology, 02(6). https://doi.org/10.55041/ijsmt.v2i6.083

Patra, D. "Data-Driven Taxi Fare Analysis Using Microsoft Power BI: Design, Implementation And Business Insights." International Journal of Science, Strategic Management and Technology, vol. 02, no. 6, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i6.083.

Patra, D. "Data-Driven Taxi Fare Analysis Using Microsoft Power BI: Design, Implementation And Business Insights." International Journal of Science, Strategic Management and Technology 02, no. 6 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i6.083.

References
Microsoft Corporation, Microsoft Power BI Documentation, Microsoft Learn.

[2] Microsoft Corporation, Power Query Documentation, Microsoft Learn.

[3] Microsoft Corporation, Data Analysis Expressions (DAX) Documentation, Microsoft Learn.

[4] Alberto Ferrari and Marco Russo, The Definitive Guide to DAX, Microsoft Press, 2019.

[5] Marco Russo and Alberto Ferrari, Analysing Data with Power BI and Power Pivot for Excel, Microsoft Press, 2017.

[6] Reza Rad, Power BI from Rookie to Rock Star, RADACAD Publications, 2020.

[7] Ralph Kimball and Margy Ross, The Data Warehouse Toolkit, Wiley Publications, 2013.

[8] Thomas H. Davenport and Jeanne G. Harris, Competing on Analytics, Harvard Business Review Press, 2007.

[9] Provost, F. and Fawcett, T., Data Science for Business, O'Reilly Media, 2013.

[10] Sharda, R., Delen, D., and Turban, E., Business Intelligence, Analytics, and Data Science, Pearson Education, 2020.

 
Ethics and Compliance
✓ All ethical standards met
This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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