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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Enhancing Uber Taxi Dispatching Efficiency in Urban Areas used CNN and SVM hybrid technique |
| Authors Name: | Ravi Rathore , OP Kandra |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_214588
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| Published Paper Id: | IJNRD2403168 |
| Published In: | Volume 9 Issue 3, March-2024 |
| DOI: | |
| Abstract: | the system described in the paper focuses on the analysis of an Uber dataset, which captures important information about Uber pickups in a city. The dataset includes key details such as date, time, and geographical coordinates. The primary objective is to leverage the k-means clustering algorithm to categorize distinct areas within the city. This clustering approach is pivotal for enhancing taxi dispatching efficiency, reducing waiting times for both drivers and passengers. The paper acknowledges the rapid growth of the industry and highlights the importance of effective taxi dispatching to meet the increasing demand. To predict demand across various city locations, the system employs machine learning algorithms, including a hybrid model that combines CNN and SVM, as well as the K Nearest Neighbour algorithm. The findings of the experiment demonstrate many performance indicators, including accuracy, precision, recall, and F1-score. These metrics serve as indicators of the effectiveness of the proposed models in predicting and optimizing taxi demand. The integration of machine learning techniques in the Uber dataset analysis demonstrates the potential for improving the overall efficiency and responsiveness of ride-sharing services in urban environments. |
| Keywords: | CNN, SVM, Uber Taxi, Machine Learning ,Deep Leaning ,KNN |
| Cite Article: | "Enhancing Uber Taxi Dispatching Efficiency in Urban Areas used CNN and SVM hybrid technique", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 3, page no.b615-b622, March-2024, Available :http://www.ijnrd.org/papers/IJNRD2403168.pdf |
| Downloads: | 00043 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 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:IJNRD2403168 Registration ID: 214588 Published In: Volume 9 Issue 3, March-2024 DOI (Digital Object Identifier): Page No: b615-b622 Country: indore , m.p., India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2403168 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2403168 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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