ACCURATE RIDE REQUEST FORECASTING OPTIMIZATION
International Journal of Computer Techniques – Volume 12 Issue 1, 2025
IJCT ISSN 2394-2231
M. VILAS1, R. SHIRISHA2, P. BHUCHENDHAR REDDY3, K. AKSHITH REDDY4, S. SIRIDHAR5
1Dept. of CSE Engineering, Siddhartha Institute of Technology and Sciences, Email: 21tq1a6711@siddhartha.co.in
2Assistant Professor, Dept. of CSE Engineering, Siddhartha Institute of Technology and Sciences, Email: shirisharangu.cse@siddhartha.co.in
3Dept. of CSE Engineering, Siddhartha Institute of Technology and Sciences, Email: 21tq1a6728@siddhartha.co.in
4Dept. of CSE Engineering, Siddhartha Institute of Technology and Sciences, Email: 21tq1a6734@siddhartha.co.in
5Dept. of CSE Engineering, Siddhartha Institute of Technology and Sciences, Email: 21tq5a6709@siddhartha.co.in
Abstract
Accurate ride request forecasting is critical for optimizing operations in ride-sharing and on-demand transportation services. By predicting future demand with precision, service providers can efficiently allocate vehicles, reduce idle time, minimize operational costs, and enhance the overall user experience. This study explores the use of advanced predictive modeling techniques, including machine learning algorithms and time-series forecasting, to improve the accuracy of ride request predictions. Published in the IJCT JOURNAL, this study offers low publication fees and fast publication services, contributing to the high impact factor of the journal.
Keywords
Ride Request Forecasting, Predictive Modeling, Machine Learning Algorithms, Time-Series Forecasting, IJCT JOURNAL, International Journal of Research Publication and Reviews, Fast Publication Journal, Low Publication Fees
References
- Yang, Y., El-Khamra, Y., & Zaman, T. (2020). “A spatio-temporal approach to ride-hailing demand forecasting using machine learning.” Spatio-temporal data modeling for ride-hailing demand prediction.
- Shekhar, S., Williams, J., et al. (2019). “Weather-aware ride-hailing demand prediction using deep learning.” The influence of weather factors on ride-hailing demand.
- Tang, Y., & Cui, Z. (2019). “Dynamic pricing in ride-sharing platforms: A machine learning approach.” Real-time optimization of pricing strategies.
- Hyndman, R. J., & Athanasopoulos, G. (2018). “Forecasting: Principles and Practice.” Foundational concepts in time-series forecasting, applicable to ride request forecasting.
- Ke, J., Zheng, H., et al. (2017). “Short-term forecasting of passenger demand in ride-sharing platforms using spatio-temporal graph convolutional networks.” Combining spatial and temporal patterns in demand forecasting.
- Böcker, L., Dijst, M., & Prillwitz, J. (2013). “Impact of Everyday Weather on Individual Daily Travel Behaviors in Perspective: A Literature Review.” Analysis of weather’s influence on travel demand and behavior.
- Banerjee, S., Johari, R., & Riquelme, C. (2016). “Dynamic Pricing in Ride-Sharing Platforms.”
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