Commenced in January 2007
Paper Count: 30067
Road Traffic Accidents Analysis in Mexico City through Crowdsourcing Data and Data Mining Techniques
Abstract: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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1340532Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF
 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. 40.
 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.