Combining Geospatial Tools and Machine Learning to Map Predicted Crash Locations
Imagine you’re a state trooper and a vehicle collision has just occurred in your area. You need to get to the location as quickly as possible, attend to injured motorists, clear the road, and get traffic moving again to prevent additional crashes. Where would you position your patrol car to minimize response time?
Resultant partnered with the Indiana State Police (ISP), the Indiana Department of Transportation (INDOT), and the Management Performance Hub (MPH) to create an interactive web-based map that provides law enforcement and the general public relevant information about where crashes have occurred in the past and where they are more likely to occur in the future.
Consolidating Data Sources
Data is the engine behind every data science project. Multiple data sources were brought together by Resultant and its partners to power the Daily Crash Prediction Map, including Automated Reporting Information Exchange System (ARIES), U.S. Census and County Business Patterns (CBP), Average Annual Daily Traffic (AADT) from INDOT, and more.
Using Geospatial Tools
Learn how the Daily Crash Prediction Map uses data from the past to predict the future. After sub-dividing the state into one-kilometer squares, the map color codes each square based on the probability of a crash occurring during a three-hour period in the future, like a weather map.
Solving Problems while Preserving Privacy
For projects with data sensitivity, Resultant has developed a proprietary geospatial masking algorithm to preserve the privacy of individual data points while, at the same time, collectively using the data to draw key insights and provide actionable and useful information to system users and administrators.
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