Road Traffic Accidents Analysis in Mexico City through Crowdsourcing Data and Data Mining Techniques
Road traffic accidents are among the principal causes of
traffic congestion, causing human losses, damages to health and the
environment, economic losses and material damages. Studies about
traditional road traffic accidents in urban zones represents very high
inversion of time and money, additionally, the result are not current.
However, nowadays in many countries, the crowdsourced GPS based
traffic and navigation apps have emerged as an important source
of information to low cost to studies of road traffic accidents and
urban congestion caused by them. In this article we identified the
zones, roads and specific time in the CDMX in which the largest
number of road traffic accidents are concentrated during 2016. We
built a database compiling information obtained from the social
network known as Waze. The methodology employed was Discovery
of knowledge in the database (KDD) for the discovery of patterns
in the accidents reports. Furthermore, using data mining techniques
with the help of Weka. The selected algorithms was the Maximization
of Expectations (EM) to obtain the number ideal of clusters for the
data and k-means as a grouping method. Finally, the results were
visualized with the Geographic Information System QGIS.
 INEGi. http://www.inegi.gob.mx., September 2016.
 Manjarrez, P. L., Vadillo, I. G. R., & Grajales, E. B. (2000). Transporte
urbano, movilidad cotidiana y ambiente en el modelo de ciudad
sostenible: bases conceptuales. Plaza y Valds, SA de CV.
 Fire, M., Kagan, D., Puzis, R., Rokach, L., & Elovici, Y. (2012,
November). Data mining opportunities in geosocial networks for
improving road safety. In Electrical & Electronics Engineers in Israel
(IEEEI), 2012 IEEE 27th Convention of (pp. 1-4). IEEE.
 Caimmi, B., Vallejos, S., Berdun, L., Soria, A´ ., Amandi, A., & Campo, M.
(2016, June). Detecci´on de incidentes de tr´ansito en Twitter. In Biennial
Congress of Argentina (ARGENCON), 2016 IEEE (pp. 1-6). IEEE.
 Mining, D., & Kulikov, O. (2009). Data Mining Social Networks.
 Kwak, H., Lee, C., Park, H., & Moon, S. (2010, April). What is Twitter, a
social network or a news media?. In Proceedings of the 19th international
conference on World wide web (pp. 591-600). ACM.
 R. F. Estrada-S, A. Molina, A. Perez-Espinosa, A. L. Reyes-C, J. L.
Quiroz-F, and E. Bravo-G, Zonification of Heavy Traffic in Mexico City.
in Proceedings of the International Conference on Data Mining (DMIN).
The Steering Committee of The World Congress in Computer Science,
Computer Engineering and Applied Computing (WorldComp), 2016, p.
 QGis, D. T. (2011). Quantum GIS geographic information system. Open
source geospatial Foundation project, 45.
 Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). The KDD process
for extracting useful knowledge from volumes of data. Communications
of the ACM, 39(11), 27-34.
 Waze Web. https://www.waze.com/es-419/livemap
 Shumaker, B. P., & Sinnott, R. W. (1984). Astronomical computing:
1. Computing under the open sky. 2. Virtues of the haversine. Sky and
telescope, 68, 158-159.
 L´opez, J. M. M., & Herrera, J. G. (2006). T´ecnicas de An´alisis de Datos
Aplicaciones Pr´acticas utilizando Microsoft Excel y Weka. Universidad
Carlos III de Madrid. Pag, 99, 125.
 Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining:
Practical machine learning tools and techniques. Morgan Kaufmann.