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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: | A TRAJECTORY ASSESSMENT SURVEY FOR PREDICTING FUTURE DISTRIBUTION USING CLUSTERED DATA |
| Authors Name: | D.S.Eunice Little Dani , Dr.R. Shalini |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_204443
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| Published Paper Id: | IJNRD2308393 |
| Published In: | Volume 8 Issue 8, August-2023 |
| DOI: | http://doi.one/10.1729/Journal.35978 |
| Abstract: | The growth of apps for location-based services has had a significant impact on the domain. The use of smart mobile terminals and the quick advancement of global positioning technology has made data on trajectories available. Location-based services to be offered like automobile scheduling and estimations of the state of the roads, the majority of technology companies will employ trajectory data. This method uses a partial trajectory query to estimate a vehicle’s-entire source-to-destination route. Regarding the complex road network, the suggested framework is capable of handling incredibly enormous amounts of data. Temporal data was analyzed and the full trajectory was predicted using a deep learning model called Long Short Term Memory (LSTM). Quick update times, high dimensionality, and a significant amount of information can be mined based on this kind of data. The grouping of comparable utilization of trajectory data to handle vast amounts of data, which aids in limiting the search space. Using measures like one-step forecast accuracy and average distance error, the favored strategy is contrasted with other published studies. When compared to other published results, the Clustered LSTM technique we present outperforms them in both parameters. A clustering-based prediction model is suggested as the best way to handle more amounts of data accurately. The findings of this work contribute to the advancement of route prediction, traffic control, and location-based recommendation systems are examples of navigation systems. |
| Keywords: | Real-time prediction, large-scale trajectory data, recurrent neural network, long-term trajectory prediction; |
| Cite Article: | "A TRAJECTORY ASSESSMENT SURVEY FOR PREDICTING FUTURE DISTRIBUTION USING CLUSTERED DATA", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 8, page no.d704-d707, August-2023, Available :http://www.ijnrd.org/papers/IJNRD2308393.pdf |
| Downloads: | 000118764 |
| 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:IJNRD2308393 Registration ID: 204443 Published In: Volume 8 Issue 8, August-2023 DOI (Digital Object Identifier): http://doi.one/10.1729/Journal.35978 Page No: d704-d707 Country: CHENNAI, TAMILNADU, INDIA Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2308393 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2308393 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
Journal Starting Year (ESTD) : 2016
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