Abstract

The main aim of this paper is to analyze the road traffic accidents in metro-politian city level at all intersection points. Analysis shows that the distribution of road accidental deaths and injuries in cities varies according to age, month and time. To develop  the system that would avoid  the accident  by sending the  notification whether the area is most  traffic and there are so many accidents occurred in that place .The most accident precaution systems are available but those are not enough to users, so this new  system may bring comfort zone to the users.  Already know that high number of accidents is happened because of hightraffic at peak hours.Reason of traffic was no of vehicles are increased.so the main concept of the system has to be done using the no of vehicles at every zone. And we use some clustering methods to denote that which zone was in active and un-active state .The userwho travelling in the night that should be most useful for them.

Keywords

Traffic accidents, Clustering, Flash board, MATLAB, number of vehicles,

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References

  1. Traffic risk mining from heterogeneous road statistics”, in Proc.IEEE Int. Conf. Data Sci. Adv. Anal., 2015, pp. 1–10.
  2. Traffic density-based discovery of hot routes in road networks,” in Advances in Spatial and Temporal Databases. SSTD (Lecture Notes in Computer Science), vol. 4605.Springer, 2007, pp. 441–459.
  3. Inferring gas consumption and pollution emission of vehicles throughout a city,” in Proc. KDD, 2014, pp. 1027–1036.
  4. Mining road traffic accident data improve safety: Role of road-related factors on accident severity in Ethiopia,” inProc. AAAI Artif. Intell. Develop. (AI-D), 2010.
  5. Pardillo-MayoraJM, Dominguez-Lira CA, Jurado-Pina R. Empirical calibration of a roadside hazardousness index for Spanish two-lane rural roads . Accid Anal Prev. 2010;42:2018
  6. Rovsek V, Batista M, Bogunovic B. Identifying the key risk factors of traffic accident injury severity on Slovenian roads using a non-parametric classification tree, transport. UK: TaylorandFrancis2014.