Home| Contact Us| New Journals| Browse Journals| Journal Prices| For Authors|

Print ISSN: 0976-898X
Online ISSN:
0976-8998


  About JITR
  DLINE Portal Home
Home
Aims & Scope
Editorial Board
Current Issue
Next Issue
Previous Issue
Sample Issue
Upcoming Conferences
Self-archiving policy
Alert Services
Be a Reviewer
Publisher
Paper Submission
Subscription
Contact us
 
  How To Order
  Order Online
Price Information
Request for Complimentary
Print Copy
 
  For Authors
  Guidelines for Contributors
Online Submission
Call for Papers
Author Rights
 
 
RELATED JOURNALS
Journal of Digital Information Management (JDIM)
International Journal of Computational Linguistics Research (IJCL)
International Journal of Web Application (IJWA)

 

 
Journal of Information Technology Review
 

Spark Big Data Platform to Manage City Traffic
Pilar Rey del Castillo
Instituto de Estudios Fiscales Avda.Cardenal Herrera Oria 378 28035 Madrid Spain
Abstract: Electronic sensors are able to generate statistical data based huge amount of data files. This is possible when the traffic dataset are available in open data portal. Electronic data is produced from traffic sensors and serve as a rich source of information, that provides speed, vehicle count and so on. It is important that traffic data needs to be processed at the micro-level using complex workloads. Thus the normal data processing tasks require big data specific tools. Initially we have used a few stages in producing short-term indicators of the evolution of the traffic flow variable in a city using the Spark big data platform. With the help of the data on the sensors’ geographical location, the indicators are then analyzed to assess the impact of some recent local government measures used to ease the pollution and traffic flow.
Keywords: Big Data, Short-term Indicators, Spark Platform, Traffic Measures Spark Big Data Platform to Manage City Traffic
DOI:https://doi.org/10.6025/jitr/2020/11/3/94-104
Full_Text   PDF 3.26 MB   Download:   290  times
References:

[1] Box, G.E., Jenkins, G. M., Reinsel, G. (1970). Time series analysis: forecasting and control holden-day san francisco. BoxTime Series Analysis: Forecasting and Control Holden Day, 1970.
[2] Box, G.E., Tiao, G. C. Intervention analysis with applications to economic and environmental problems. Journal of the American Statistical association, 70 (349), 70–79.
[3] Chen, C., Liu, L.M. (1993). Joint estimation of model parameters and outlier effects in time series. Journal of the American Statistical Association, 88 (421), 284–297.
[4] MacQueen, J., et al. (1967). Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability. vol. 1, p 281–297. Oakland, CA, USA (1967)
[5] Medina, J. C., Benekohal, R.F., Ramezani, H. (2012). Field evaluation of smart sensor vehicle detectors at intersections volume 1: Normal weather conditions. Tech. rep.
[6] Mimbela, L.E.Y., Klein, L.A. (2000). Summary of vehicle detection and surveillance technologies used in intelligent transportation systems.
[7] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al. (2011). Scikit-learn: Machine learning in python. Journal of machine learning research, 12 (Oct), 2825–2830.
[8] Schmidhuber, J., Hochreiter, S. (1997). Long short-term memory. Neural Comput, 9(8), 1735–1780. 
[09] Stone, R., Prais, S. (1952). Systems of aggregative index numbers and their compatibility. The Economic Journal , 62 (247), 565–583.
[10] Team, P. C. (2017). Python: A dynamic, open source programming language, python software foundation.
[11] Thorndike, R.L. (1953). Who belongs in the family?, Psychometrika, 18(4), 267–276.
[12] Welch, P. (1967). The use of fast fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Transactions on audio and electroacoustics, 15(2), 70–73.
[13] Zaharia, M., Xin, R. S., Wendell, P., Das, T., Armbrust, M., Dave, A., Meng, X., Rosen, J., Venkataraman, S., Franklin, M.J., et al. (2016). Apache spark: a unified engine for big data processing. Communications of the ACM, 59(11), 56–65.
[14] Zhang, K., Batterman, S. (2013). Air pollution and health risks due to vehicle traffic. Science of the total Environment 450, 307–316.


Home | Aim & Scope | Editorial Board | Author Guidelines | Publisher | Subscription | Previous Issue | Contact Us |Upcoming Conferences|Sample Issues|Library Recommendation Form|

 

Copyright © 2011 dline.info