{"title":"Road Traffic Accidents Analysis in Mexico City through Crowdsourcing Data and Data Mining Techniques","authors":"Gabriela V. Angeles Perez, Jose Castillejos Lopez, Araceli L. Reyes Cabello, Emilio Bravo Grajales, Adriana Perez Espinosa, Jose L. Quiroz Fabian","volume":140,"journal":"International Journal of Computer and Information Engineering","pagesStart":604,"pagesEnd":609,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10009340","abstract":"Road traffic accidents are among the principal causes of
\r\ntraffic congestion, causing human losses, damages to health and the
\r\nenvironment, economic losses and material damages. Studies about
\r\ntraditional road traffic accidents in urban zones represents very high
\r\ninversion of time and money, additionally, the result are not current.
\r\nHowever, nowadays in many countries, the crowdsourced GPS based
\r\ntraffic and navigation apps have emerged as an important source
\r\nof information to low cost to studies of road traffic accidents and
\r\nurban congestion caused by them. In this article we identified the
\r\nzones, roads and specific time in the CDMX in which the largest
\r\nnumber of road traffic accidents are concentrated during 2016. We
\r\nbuilt a database compiling information obtained from the social
\r\nnetwork known as Waze. The methodology employed was Discovery
\r\nof knowledge in the database (KDD) for the discovery of patterns
\r\nin the accidents reports. Furthermore, using data mining techniques
\r\nwith the help of Weka. The selected algorithms was the Maximization
\r\nof Expectations (EM) to obtain the number ideal of clusters for the
\r\ndata and k-means as a grouping method. Finally, the results were
\r\nvisualized with the Geographic Information System QGIS.","references":"[1] INEGi. http:\/\/www.inegi.gob.mx., September 2016.\r\n[2] Manjarrez, P. L., Vadillo, I. G. R., & Grajales, E. B. (2000). Transporte\r\nurbano, movilidad cotidiana y ambiente en el modelo de ciudad\r\nsostenible: bases conceptuales. Plaza y Valds, SA de CV. [3] Fire, M., Kagan, D., Puzis, R., Rokach, L., & Elovici, Y. (2012,\r\nNovember). Data mining opportunities in geosocial networks for\r\nimproving road safety. In Electrical & Electronics Engineers in Israel\r\n(IEEEI), 2012 IEEE 27th Convention of (pp. 1-4). IEEE.\r\n[4] Caimmi, B., Vallejos, S., Berdun, L., Soria, A\u00b4 ., Amandi, A., & Campo, M.\r\n(2016, June). Detecci\u00b4on de incidentes de tr\u00b4ansito en Twitter. In Biennial\r\nCongress of Argentina (ARGENCON), 2016 IEEE (pp. 1-6). IEEE.\r\n[5] Mining, D., & Kulikov, O. (2009). Data Mining Social Networks.\r\n[6] Kwak, H., Lee, C., Park, H., & Moon, S. (2010, April). What is Twitter, a\r\nsocial network or a news media?. In Proceedings of the 19th international\r\nconference on World wide web (pp. 591-600). ACM.\r\n[7] R. F. Estrada-S, A. Molina, A. Perez-Espinosa, A. L. Reyes-C, J. L.\r\nQuiroz-F, and E. Bravo-G, Zonification of Heavy Traffic in Mexico City.\r\nin Proceedings of the International Conference on Data Mining (DMIN).\r\nThe Steering Committee of The World Congress in Computer Science,\r\nComputer Engineering and Applied Computing (WorldComp), 2016, p.\r\n40.\r\n[8] QGis, D. T. (2011). Quantum GIS geographic information system. Open\r\nsource geospatial Foundation project, 45.\r\n[9] Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). The KDD process\r\nfor extracting useful knowledge from volumes of data. Communications\r\nof the ACM, 39(11), 27-34.\r\n[10] Waze Web. https:\/\/www.waze.com\/es-419\/livemap\r\n[11] Shumaker, B. P., & Sinnott, R. W. (1984). Astronomical computing:\r\n1. Computing under the open sky. 2. Virtues of the haversine. Sky and\r\ntelescope, 68, 158-159.\r\n[12] L\u00b4opez, J. M. M., & Herrera, J. G. (2006). T\u00b4ecnicas de An\u00b4alisis de Datos\r\nAplicaciones Pr\u00b4acticas utilizando Microsoft Excel y Weka. Universidad\r\nCarlos III de Madrid. Pag, 99, 125.\r\n[13] Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining:\r\nPractical machine learning tools and techniques. Morgan Kaufmann.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 140, 2018"}