Traffic Crash Analysis System
Carl Reim, Hennepin County Public Works
Locating, evaluating, and analyzing traffic crash data is an integral part of transportation planning and engineering. These location-based phenomena can help highlight facilities that require a safety review with the intent of promoting safe spaces for all modes of transportation, which aligns with Hennepin County’s Towards Zero Deaths initiative. The Public Works department has been working with the GIS team to develop a Crash Data System that allows for staff to view, correct and analyze crashes, providing safety professionals the information needed to for data-driven decision making and prioritization of roadway safety improvements.
By extracting crash data from the Minnesota Department of Public Safety, Hennepin County safety staff can transform and load it into Portal for ArcGIS where staff can apply it more specifically to their inventory of roads and intersections. Developing this crash system provides the opportunity to validate and correct crash information that may have been reported improperly, resulting in higher accuracy and reliability. Additionally, the end products can be customizable to the preferences of leadership and decision-makers.
Esri’s Operations Dashboard provides a platform to display crashes, discover patterns and highlight locations for identification and resolution of reactive traffic safety issues. Its flexibility allows safety staff to explore where, when and what type of crashes are occurring along roadways and at intersections. Additionally, Microsoft’s Power BI provides valuable reporting tools available for staff through the organization’s SharePoint system. This unique project requires strong collaboration - both internally and externally - and the utilization of various techniques and software. However, the end-product helps to prioritize safety investments to improve traffic safety in an efficient and data driven manner.
Evaluating perceptions of cycling safety in Eau Claire, WI through spatial analysis
Matthew St. Ores, Savanna Grunzke, University of Wisconsin - Eau Claire
Bikeable cities provide numerous benefits to the environment, individual citizens, and communities at large, yet safety is consistently cited as a major barrier to using a bike for transportation. Through a custom web mapping application, we surveyed citizens on their perceptions of unsafe cycling locations in Eau Claire, Wisconsin. Then, we use spatial statistics to identify clusters of problematic areas within the city. Following this, we use machine learning models to describe how perceptions relate to characteristics of the built environment. While the results have tangible transportation planning implications for the city, more importantly, the methods could easily be replicated in other municipalities to discover related patterns.
29 окт 2024
