Full Paper View Go Back

Atmospheric Change on the Geographical Theme Finding Of Different Functions on Human Mobility

S. Ramana1 , S. Sabitha2 , R. Senthil Kumar3 , T. Senthil Prakash4

Section:Research Paper, Product Type: Isroset-Journal
Vol.6 , Issue.2 , pp.134-151, Apr-2018


CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i2.134151


Online published on Apr 30, 2018


Copyright © S. Ramana, S. Sabitha, R. Senthil Kumar, T. Senthil Prakash . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

View this paper at   Google Scholar | DPI Digital Library


XML View     PDF Download

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: S. Ramana, S. Sabitha, R. Senthil Kumar, T. Senthil Prakash, “Atmospheric Change on the Geographical Theme Finding Of Different Functions on Human Mobility,” International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.134-151, 2018.

MLA Style Citation: S. Ramana, S. Sabitha, R. Senthil Kumar, T. Senthil Prakash "Atmospheric Change on the Geographical Theme Finding Of Different Functions on Human Mobility." International Journal of Scientific Research in Computer Science and Engineering 6.2 (2018): 134-151.

APA Style Citation: S. Ramana, S. Sabitha, R. Senthil Kumar, T. Senthil Prakash, (2018). Atmospheric Change on the Geographical Theme Finding Of Different Functions on Human Mobility. International Journal of Scientific Research in Computer Science and Engineering, 6(2), 134-151.

BibTex Style Citation:
@article{Ramana_2018,
author = {S. Ramana, S. Sabitha, R. Senthil Kumar, T. Senthil Prakash},
title = {Atmospheric Change on the Geographical Theme Finding Of Different Functions on Human Mobility},
journal = {International Journal of Scientific Research in Computer Science and Engineering},
issue_date = {4 2018},
volume = {6},
Issue = {2},
month = {4},
year = {2018},
issn = {2347-2693},
pages = {134-151},
url = {https://www.isroset.org/journal/IJSRCSE/full_paper_view.php?paper_id=623},
doi = {https://doi.org/10.26438/ijcse/v6i2.134151}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i2.134151}
UR - https://www.isroset.org/journal/IJSRCSE/full_paper_view.php?paper_id=623
TI - Atmospheric Change on the Geographical Theme Finding Of Different Functions on Human Mobility
T2 - International Journal of Scientific Research in Computer Science and Engineering
AU - S. Ramana, S. Sabitha, R. Senthil Kumar, T. Senthil Prakash
PY - 2018
DA - 2018/04/30
PB - IJCSE, Indore, INDIA
SP - 134-151
IS - 2
VL - 6
SN - 2347-2693
ER -

438 Views    235 Downloads    113 Downloads
  
  

Abstract :
Over the last century, the planet has metaphorically contracted as transport has developed to meet the demands of the populous. Global participation in this expansion has been disproportionate as the driving force for transport demand is ultimately economic growth, which in itself results in an increased need for travel. The activities of the transport systems in most countries are sensitive to a range of weather extremes, including those related to precipitation, thunderstorms, temperature, winds, visibility and sea level. The impact of climate, climate variability and climate change, in particular the impact of these extremes on transport systems and adaptation measures are discussed. The foundation of climate services to assist informed decision-making for climate change adaptation and travelling time prediction, planning and designing, which require close collaboration among a wide range of Disciplines and the engagement of the users such as the transport systems’ communities by using the weather-traffic indices extracted have been validated to be surprisingly consistent with real world observations.

Key-Words / Index Term :
Weather prediction, Traffic parameters count, Support vector machine, Neural networks, Factor analysis

References :
[1] Y. Ding, Y. Li, K. Deng, H. Tan, M. Yuan, and L. M. Ni, “Dissecting regional weather-traffic sensitivity throughout a city,” in 15th IEEE International Conference on Data Mining, ICDM 2015, Atlantic City, NJ, USA, November 14-17, 2015, pp. 739–744.
[2] Y. Zheng, L. Capra, O. Wolfson, and H. Yang, “Urban computing: Concepts, methodologies, and applications,” ACM Transaction on Intelligent Systems and Technology, 2014.
[3] A. Saegusa and Y. Fujiwara, “A study on forecasting road surface conditions based on weather and road surface data,” IEICE Transactions, vol. 90-D, no. 2, pp. 509–516, 2007.
[4] M. A. Abdel-Aty and R. Pemmanaboina, “Calibrating a real-time traffic crash-prediction model using archived weather and its traffic data,” IEEE Transactions on Intelligent Transportation Systems, vol. 7, no. 2, pp. 167–174, 2006.
[5] S. Dunne and B. Ghosh, “Weather adaptive traffic prediction using neurowavelet models,” IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 1, pp. 370–379, 2013.
[6] M. J. Koetse and P. Rietveld, “The impact of climate change and weather on transport: An overview of empirical findings,” Transportation Research Part D: Transport and Environment, vol. 14, no. 3, pp. 205– 221, 2009.
[7] . Talking about smart transportation from the rainstorm of Beijing, “http://info.secu.hc360.com/2012/07/300822649026.shtml.”
[8] Y. Zheng, F. Liu, and H.-P. Hsieh, “U-air: when urban air quality inference meets big data,” in KDD, 2013, pp. 1436–1444.
[9] S. Liu, Y. Liu, L. M. Ni, J. Fan, and M. Li, “Towards mobility-based clustering,” in KDD, 2010, pp. 919–928.
[10] J. Yuan, Y. Zheng, and X. Xie, “Discovering regions of different functions in a city using human mobility and pois,” in KDD, 2012, pp. 186–194.
[11] F. Zhang, D. Wilkie, Y. Zheng, and X. Xie, “Sensing the pulse of urban refueling behavior,” in UbiComp, 2013, pp. 13–22.
[12] K. Zheng, Y. Zheng, N. J. Yuan, and S. Shang, “On discovery of gathering patterns from trajectories,” in ICDE, 2013, pp. 242–253.
[13] L. A. Tang, Y. Zheng, J. Yuan, J. Han, A. Leung, C.-C. Hung, and W.-C. Peng, “On discovery of traveling companions from streaming trajectories,” in ICDE, 2012, pp. 186–197.
[14] Y. Zheng, “Methodologies for cross-domain data fusion: An overview,” IEEE Trans. Big Data, vol. 1, no. 1, pp. 16–34, 2015.
[15] F. Aurenhammer, “Voronoi diagrams - a survey of a fundamental geometric data structure,” ACM Comput. Surv., vol. 23, no. 3, pp. 345–405, 1991.

Authorization Required

 

You do not have rights to view the full text article.
Please contact administration for subscription to Journal or individual article.
Mail us at  support@isroset.org or view contact page for more details.

Go to Navigation