Real-Time Cab Fare and ETA Prediction Using API Integration

Year : 2025 | Volume : 16 | Issue : 02 | Page : 08-15
    By

    Deep Rane,

  • Adesh Pagar,

  • Harshada Kadam,

  • Vedant Raut,

  • Sharayu Patil,

  1. Student, Department of Computer Engineering, Vishwaniketan’s Institute of Management Entrepreneurship and Engineering Technology (ViMEET), Kumbhivli, Maharashtra, India
  2. Student, Department of Computer Engineering, Vishwaniketan’s Institute of Management Entrepreneurship and Engineering Technology (ViMEET), Kumbhivli, Maharashtra, India
  3. Student, Department of Computer Engineering, Vishwaniketan’s Institute of Management Entrepreneurship and Engineering Technology (ViMEET), Kumbhivli, Maharashtra, India
  4. Student, Department of Computer Engineering, Vishwaniketan’s Institute of Management Entrepreneurship and Engineering Technology (ViMEET), Kumbhivli, Maharashtra, India
  5. Professor, Department of Computer Engineering, Vishwaniketan’s Institute of Management Entrepreneurship and Engineering Technology (ViMEET), Kumbhivli, Maharashtra, India

Abstract

The exponential proliferation of ride-hailing platforms has necessitated the formulation of sophisticated and highly responsive predictive models for cab fare estimation and estimated time of arrival (ETA) computation. This work elucidates a robust framework leveraging real-time application programming interface (API) integration from Uber and Ola within a Flutter-based ecosystem to enhance predictive analytics. By assimilating real-time geospatial data, dynamic pricing algorithms, and latency-optimized API responses, this study investigates the empirical correlation between API-driven predictions and their computational efficacy. The methodology incorporates high-precision geolocation tracking, asynchronous data retrieval mechanisms, and heuristic fare comparison models to enable optimized decision-making for end-users. Empirical analysis reveals that API-driven real-time estimations substantially mitigate fare disparities and enhance trip planning efficiency. Furthermore, this study discusses the challenges of API rate limitations, data latency, and network dependency while proposing adaptive machine learning enhancements for future scalability. With the increasing demand for ride-hailing services, accurate prediction of cab fares and ETA has become crucial for enhancing user experience and optimizing operations. This work explores a real-time system that integrates multiple APIs, including mapping, traffic, and weather data, to predict cab fares and ETAs with high accuracy. By leveraging machine learning models and real-time data from sources such as Google Maps API, Open Weather API, and ride-hailing service APIs, the proposed system dynamically adjusts predictions based on traffic congestion, weather conditions, and surge pricing. The study evaluates different regression and deep learning models to improve fare estimations while minimizing ETA deviations. Performance analysis demonstrates that API-driven real-time predictions significantly enhance the accuracy of traditional fare and ETA models. This work provides a scalable framework for intelligent transportation systems, benefiting both service providers and customers.

Keywords: Real-time application programming interface (API) integration, ride-hailing services, cab fare prediction, estimated time of arrival (ETA), geospatial analytics, machine learning, dynamic pricing, predictive modelling, Uber API, Ola API, geolocation tracking, asynchronous data retrieval

[This article belongs to Journal of Computer Technology & Applications ]

How to cite this article:
Deep Rane, Adesh Pagar, Harshada Kadam, Vedant Raut, Sharayu Patil. Real-Time Cab Fare and ETA Prediction Using API Integration. Journal of Computer Technology & Applications. 2025; 16(02):08-15.
How to cite this URL:
Deep Rane, Adesh Pagar, Harshada Kadam, Vedant Raut, Sharayu Patil. Real-Time Cab Fare and ETA Prediction Using API Integration. Journal of Computer Technology & Applications. 2025; 16(02):08-15. Available from: https://journals.stmjournals.com/jocta/article=2025/view=208006


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Regular Issue Subscription Review Article
Volume 16
Issue 02
Received 04/02/2025
Accepted 19/03/2025
Published 15/04/2025
Publication Time 70 Days
Total Visits: 116


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