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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: April 2026
Volume 11 | Issue 4
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 119
Article Submitted : 23355
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Total Authors : 23952
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| Paper Title: | Comparative Analysis Of Machine Learning Models For Urban Shared Mobility Demand Prediction |
| Authors Name: | Kishita Deotale , Arya Ambagade , Sonia Sharma , Anjali Vyawhare , Shashwati Papulwad |
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IJRTI_210386
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| Published Paper Id: | IJRTI2604011 |
| Published In: | Volume 11 Issue 4, April-2026 |
| DOI: | |
| Abstract: | —Ride demand forecasting is essential for efficient transportation and shared mobility systems, enabling improved resource allocation, reduced waiting times, and better service quality. Although large ride hailing platforms employ advanced analytics, small scale transportation providers often lack accessible and deployable prediction solutions. This study analyzes historical ride demand data and performs a comparative evaluation of machine learning and deep learning models to identify the most effective forecasting approach. The dataset incorporates temporal attributes, seasonal patterns, holiday indicators, weather conditions, and user related factors. Exploratory data analysis and feature engineering were conducted to capture time based and holiday driven demand variations. Multiple models, including Linear Regression, Lasso Regression, Ridge Regression, Random Forest Regression, and Long Short Term Memory networks, were implemented and evaluated using standard performance metrics. Results indicate that temporal and holiday features strongly influence demand, and the LSTM model provides the highest predictive accuracy for practical forecasting applications use. |
| Keywords: | Intelligent Transportation Systems, Time Series Forecasting, Machine Learning, Deep Learning, Long Short- Term Memory, Ride Demand Prediction, Transportation Analytics |
| Cite Article: | "Comparative Analysis Of Machine Learning Models For Urban Shared Mobility Demand Prediction", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.a84-a92, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604011.pdf |
| Downloads: | 000140 |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2604011
Registration ID:210386
Published In: Volume 11 Issue 4, April-2026
DOI (Digital Object Identifier):
Page No: a84-a92 Country: Nagpur , Maharashtra , India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2604011 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2604011 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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