A TRAJECTORY ASSESSMENT SURVEY FOR PREDICTING FUTURE DISTRIBUTION USING CLUSTERED DATA

A TRAJECTORY ASSESSMENT SURVEY FOR PREDICTING FUTURE DISTRIBUTION USING CLUSTERED DATA

Registration ID: IJNRD_204443

Published ID: IJNRD2308393

DOI: http://doi.one/10.1729/Journal.35978

D.S.Eunice Little Dani , Dr.R. Shalini

Real-time prediction, large-scale trajectory data, recurrent neural network, long-term trajectory prediction;

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.

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"A TRAJECTORY ASSESSMENT SURVEY FOR PREDICTING FUTURE DISTRIBUTION USING CLUSTERED DATA", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.8, Issue 8, page no.d704-d707, August-2023, Available :https://ijnrd.org/papers/IJNRD2308393.pdf

Volume 8 Issue 8, August-2023

Pages : d704-d707

Paper Reg. ID: IJNRD_204443

Published Paper Id: IJNRD2308393

Downloads: 000121138

Research Area: Computer Science & Technology 

Country: CHENNAI, TAMILNADU, INDIA

Published Paper PDF: https://ijnrd.org/papers/IJNRD2308393.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2308393

DOI: http://doi.one/10.1729/Journal.35978

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

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

Publisher: IJNRD (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

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