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Machine Learning model to create an optimized ambulance deployment strategy in Nairobi.

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UBER NAIROBI AMBULANCE PERAMBULATION CHALLENGE

INTRODUCTION

Road traffic collisions are the number one killer of children and young adults ages 5-29, and 8th leading cause of death worldwide. Post-crash care is one of the five pillars of road safety and a critical component for reducing morbidity and mortality.

When it comes to emergency response to road accidents, every second counts. With heavy traffic patterns and the unique layout of the city, finding the best locations to position emergency responders throughout the day as they wait to be called is critical in a city like Nairobi.

GAOL

Information has been collected on thousands of traffic accidents that have occurred in Nairobi, Kenya in 2018 and 2019. For the competition, use training data (recorded crashes up to June 2019) along with supplementary data from Uber Movement, road survey data and weather patterns to identify patterns of risk across the city. Used these findings to place six virtual ambulances around the city, moving them around throughout the day with the goal of minimising the distance travelled when responding to crashes during the test period.

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Machine Learning model to create an optimized ambulance deployment strategy in Nairobi.

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