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WisTransPortal Predictive Crash Research & Development


Abstract: Recent advances in crash data collection and management in Wisconsin have afforded the opportunity to improve the effectiveness of traffic safety enforcement activities through data driven resource allocation. Initial “predictive analytics” decision support capabilities were developed during the 2017 project year and are in the process of being rolled out statewide in the form of a new heat map enabled crash analysis interface in the Community Maps system and automated crash map layers in the Wisconsin State Patrol MACH system. Backend processes for this automation were developed along with visualization capabilities to support law enforcement agency (LEA) resource allocation for a range of user defined scenarios. Longer term objectives, which will be started during the 2018 project year, are to build upon the 2017 rollout through algorithm and reporting improvements, and the development of performance measures. When completed, this project will establish a critical feedback loop between crash reporting and LEAs. It will also allow LEAs to act more proactively to prevent crashes, rather than by responding to them. Project Objectives This project will allow the UW TOPS Lab to continue researching and developing best practices for predicting where and under what conditions crashes occur. This would allow LEAs—and the State Patrol in particular—to expend resources in the most efficient manner possible by being in place where and when crashes are most likely to occur. This visibility will lessen risky driver behaviors and may also allow for better crash outcomes by lessening response times. Specific objectives will include algorithmic improvements based on performance results from the new system.

Andrea Bill - Traffic safety engineering Research Program Manager



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Chris Osbourn


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