Road Accidents Bigdata Mining and Visualization Using Support Vector Machines
Useful information has been extracted from the
road accident data in United Kingdom (UK), using data analytics
method, for avoiding possible accidents in rural and urban areas.
This analysis make use of several methodologies such as data
integration, support vector machines (SVM), correlation machines
and multinomial goodness. The entire datasets have been imported
from the traffic department of UK with due permission. The
information extracted from these huge datasets forms a basis for
several predictions, which in turn avoid unnecessary memory
lapses. Since data is expected to grow continuously over a period
of time, this work primarily proposes a new framework model
which can be trained and adapt itself to new data and make
accurate predictions. This work also throws some light on use of
SVM’s methodology for text classifiers from the obtained traffic
data. Finally, it emphasizes the uniqueness and adaptability of
SVMs methodology appropriate for this kind of research work.
[1] Department for Transport. (n.d.). Retrieved January 28, 2017, from
https://www.gov.uk/government/organisations/department-for-transport.
[2] Katie Feline, Ph.D. Proyecto Titi, Inc., Adrian McDermott, SVP
Product Development, Zendesk, KlearSky, I. M. (2016, December
27). Trifacta Wrangler — Products. Retrieved January 28, 2017, from
https://www.trifacta.com/products/wrangler/.
[3] Data Science Platform — Machine Learning. (2017, January 25).
Retrieved January 28, 2017, from https://rapidminer.com/
[4] Tableau Software. (n.d.). Retrieved January 28, 2017, from
https://www.tableau.com/.
[5] Wibisono, A., Jatmiko, W., Wisesa, H. A., Hardjono,
B., Mursanto, P. (2016). Traffic big data prediction and
visualization using Fast Incremental Model Trees-Drift
Detection (FIMT-DD). Knowledge-Based Systems, 93, 33-46.
doi:10.1016/j.knosys.2015.10.028.
[6] Shi, Q., Abdel-Aty, M. (2015). Big Data applications in real-time
traffic operation and safety monitoring and improvement on urban
expressways. Transportation Research Part C: Emerging Technologies,
58, 380-394. doi:10.1016/j.trc.2015.02.022.
[7] Taylor, P., Griffiths, N., Bhalerao, A., Xu, Z., Gelencser, A.,
Popham, T. (2015). Warwick-JLR driver monitoring dataset (DMD).
Proceedings of the 7th International Conference on Automotive User
Interfaces and Interactive Vehicular Applications - Automotive ’15.
doi:10.1145/2799250.2799286.
[8] Shanti Verma. 2016. Deciding Admission Criteria For Master of
Computer Applications Program in India using Chi-Square Test. In
Proceedings of the Second International Conference on Information
and Communication Technology for Competitive Strategies (ICTCS
’16). ACM, New York, NY, USA, , Article 103 , 4 pages.
[9] Solutions. (n.d.). Retrieved January 28, 2017, from
http://www.mathworks.com/solutions.
[10] Rajkumar, R. R.; Lee, I.; Sha, L.; and Stankovic, J. 2010.
Cyber-physical systems: the next computing revolution. In Proceedings
of the 47th Design Automation Conference, 731 736. ACM.
[11] Anantharam, P., Thirunarayan, K., Marupudi, S., Sheth, A. P.,
Banerjee, T. (2016, February). Understanding City Traffic Dynamics
Utilizing Sensor and Textual Observations. In AAAI pp. 3793-3799.
[12] Urun Dogan, Tobias Glasmachers, and Christian Igel. 2016. A unified
view on multi-class support vector classification. J. Mach. Learn. Res.
17, 1 (January 2016), 1550-1831.
[13] Vapnik. Statistical Learning Theory. John Wiley and Sons, 1998.
[14] E. Boser, I. M. Guyon, and V. N. Vapnik. A training algorithm
for optimal margin classifiers. In Proceedings of the Fifth Annual
Workshop on Computational Learning Theory (COLT 1992), pages
144152. ACM, 1992.
[15] Katy Borner. Data Visualization Literacy. In Proceedings of the 27th
ACM Conference on Hypertext and Social Media (HT 2016), pages
1-1. ACM, 2016.
[16] Road safety dataset. Retrieved January 20, 2017, from
https://data.gov.uk/dataset/road-accidents-safety-data
[1] Department for Transport. (n.d.). Retrieved January 28, 2017, from
https://www.gov.uk/government/organisations/department-for-transport.
[2] Katie Feline, Ph.D. Proyecto Titi, Inc., Adrian McDermott, SVP
Product Development, Zendesk, KlearSky, I. M. (2016, December
27). Trifacta Wrangler — Products. Retrieved January 28, 2017, from
https://www.trifacta.com/products/wrangler/.
[3] Data Science Platform — Machine Learning. (2017, January 25).
Retrieved January 28, 2017, from https://rapidminer.com/
[4] Tableau Software. (n.d.). Retrieved January 28, 2017, from
https://www.tableau.com/.
[5] Wibisono, A., Jatmiko, W., Wisesa, H. A., Hardjono,
B., Mursanto, P. (2016). Traffic big data prediction and
visualization using Fast Incremental Model Trees-Drift
Detection (FIMT-DD). Knowledge-Based Systems, 93, 33-46.
doi:10.1016/j.knosys.2015.10.028.
[6] Shi, Q., Abdel-Aty, M. (2015). Big Data applications in real-time
traffic operation and safety monitoring and improvement on urban
expressways. Transportation Research Part C: Emerging Technologies,
58, 380-394. doi:10.1016/j.trc.2015.02.022.
[7] Taylor, P., Griffiths, N., Bhalerao, A., Xu, Z., Gelencser, A.,
Popham, T. (2015). Warwick-JLR driver monitoring dataset (DMD).
Proceedings of the 7th International Conference on Automotive User
Interfaces and Interactive Vehicular Applications - Automotive ’15.
doi:10.1145/2799250.2799286.
[8] Shanti Verma. 2016. Deciding Admission Criteria For Master of
Computer Applications Program in India using Chi-Square Test. In
Proceedings of the Second International Conference on Information
and Communication Technology for Competitive Strategies (ICTCS
’16). ACM, New York, NY, USA, , Article 103 , 4 pages.
[9] Solutions. (n.d.). Retrieved January 28, 2017, from
http://www.mathworks.com/solutions.
[10] Rajkumar, R. R.; Lee, I.; Sha, L.; and Stankovic, J. 2010.
Cyber-physical systems: the next computing revolution. In Proceedings
of the 47th Design Automation Conference, 731 736. ACM.
[11] Anantharam, P., Thirunarayan, K., Marupudi, S., Sheth, A. P.,
Banerjee, T. (2016, February). Understanding City Traffic Dynamics
Utilizing Sensor and Textual Observations. In AAAI pp. 3793-3799.
[12] Urun Dogan, Tobias Glasmachers, and Christian Igel. 2016. A unified
view on multi-class support vector classification. J. Mach. Learn. Res.
17, 1 (January 2016), 1550-1831.
[13] Vapnik. Statistical Learning Theory. John Wiley and Sons, 1998.
[14] E. Boser, I. M. Guyon, and V. N. Vapnik. A training algorithm
for optimal margin classifiers. In Proceedings of the Fifth Annual
Workshop on Computational Learning Theory (COLT 1992), pages
144152. ACM, 1992.
[15] Katy Borner. Data Visualization Literacy. In Proceedings of the 27th
ACM Conference on Hypertext and Social Media (HT 2016), pages
1-1. ACM, 2016.
[16] Road safety dataset. Retrieved January 20, 2017, from
https://data.gov.uk/dataset/road-accidents-safety-data
@article{"International Journal of Information, Control and Computer Sciences:75813", author = "Usha Lokala and Srinivas Nowduri and Prabhakar K. Sharma", title = "Road Accidents Bigdata Mining and Visualization Using Support Vector Machines", abstract = "Useful information has been extracted from the
road accident data in United Kingdom (UK), using data analytics
method, for avoiding possible accidents in rural and urban areas.
This analysis make use of several methodologies such as data
integration, support vector machines (SVM), correlation machines
and multinomial goodness. The entire datasets have been imported
from the traffic department of UK with due permission. The
information extracted from these huge datasets forms a basis for
several predictions, which in turn avoid unnecessary memory
lapses. Since data is expected to grow continuously over a period
of time, this work primarily proposes a new framework model
which can be trained and adapt itself to new data and make
accurate predictions. This work also throws some light on use of
SVM’s methodology for text classifiers from the obtained traffic
data. Finally, it emphasizes the uniqueness and adaptability of
SVMs methodology appropriate for this kind of research work.", keywords = "Road accident, machine learning, support vector
machines.", volume = "10", number = "8", pages = "1578-6", }
