Road Accident are a major cause of death worldwide leading to around 10.25 lakh deaths and 5 crores injuries ever year. Road accidentsare extremely common. If you live in a sprawling metropolis like we do, chances are that we have heard about, witnessed, or even involved in one. Therefore, a system that can predict the occurrence of traffic accidents or accident-prone areas can potentially save lives. Although difficult, traffic accident prediction is not impossible. Accidents do not arise in a purely stochastic manner; their occurrence is influenced by a multitude of factors such as drivers physical conditions, car types, driving speed, traffic condition, road structure and weather. Paper is a deep learning python dynamic Routine maker which is designed to give the user a better understanding with the next day Traffic and help user to fulfill his/her sleep. Our mode Consider the following inputs speed, traffic condition, crash counts, road structure and weather to less obvious factors such as national holidays, the moon cycle and selective attention. Fortunately, several of such accident records are publicly available. Various municipal and national government in the UK have made available rich datasets of Road Traffic Accidents (RTA) and their associated factors. By exploring this government datasets and external data sources, we aim to discover patterns that predict with high accuracy tells road accident to happens.



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