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.

Authors:

• Gabriela V. Angeles Perez
• Jose Castillejos Lopez
• Araceli L. Reyes Cabello
• Emilio Bravo Grajales
• Adriana Perez Espinosa
• Jose L. Quiroz Fabian

Keywords:

• Data mining
• K-means
• road traffic accidents
• Waze
• Weka.

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