This page compares average crashes per month prior to construction (2017-2019), during construction (8/23/2021 to 6/30/2025), and post-construction (7/1/2025 to current). These metrics are calculated by summing the total crashes in the time period, dividing by the total number of days (crashes/day), and then multiplying by 30 to get crashes/month. Post-construction crashes per month is updating daily and is set up to account for new days being added and new crashes being added. A % reduction from preconstruction is provided to show the approximate reduction in crash frequency.
Because Post-construction crashes/month is updating daily, this metric is stored daily and charted in the live data tabs. The summary page will show today's value. Historical values are available in the live data.
Generally, crashes per month decreased during construction compared to preconstruction, and decreased again post-construction when compared to both during and preconstruction.
This page compares average crash cost per month prior to construction (2017-2019), during construction (8/23/2021 to 6/30/2025), and post-construction (7/1/2025 to current). These metrics are calculated by summing the total crash cost in the time period, dividing by the total number of days (crashes/day), and then multiplying by 30 to get cost/month. Post-construction crash cost per month is updating daily and is set up to account for new days being added and new crashes being added. A % reduction from preconstruction is provided to show the approximate reduction in crash cost.
Because Post-construction crash cost/month is updating daily, this metric is stored daily and charted in the live data tabs. The summary page will show today's value. Historical values are available in the live data.
Generally, crash cost per month decreased during construction compared to preconstruction, and decreased again post-construction when compared to both during and preconstruction. This indicates both a reduction in crash frequency and severity.
K and A crashes have a project normalized value of $2,693,777 to reduce significant crash cost influence from K type crashes. B crashes have an associated cost of $248,690. C crashes have an associated cost of $141,821. PD crashes have an associated cost of $18,576. Information on crash costs can be found at the following link:
https://wisconsindot.gov/rdwy/fdm/fd-11-38.pdf
This tab is automated and plots new data daily at 6am. These charts should be used to monitor post-construction crash performance over time. After enough time has passed post-construction, this charting will stabilize to give a good overview of post-construction performance. The outlined methodology accounts for the transportal crash reporting delay. This tab is updating automatically and taking new crashes into account daily.
This statistic calculates a crashes/day metric that is then multiplied by 30 to report crashes/month from the time period of 7/1/2025 to Current Date -28 days. There is a 28 day delay to account for reporting delays. The post-construction crashes/month metric is compared to a preconstruction crashes/month metric between the time period of 2017-2019. This metric changes daily as more days pass post-construction and as crashes occur in the corridor. There were 21.6 crashes per month during the preconstruction window of 2017-2019.
The daily statistic is reported, as well as an average for the charted time period. Use the tooltip feature to see what the calculated date range is for each point.
Similar to the crash trends, cost trends calculates a cost/month metric post-construction and compares to a cost/month for preconstruction. K and A crashes have a project normalized value of $2,693,777 (WisDOT methodology). B crashes have an associated cost of $248,690. C crashes have an associated cost of $141,821. PD crashes have an associated cost of $18,576. The most recent 28 days of data are excluded to account for reporting delays.
Tab used for developer use to verify TOPS lab API and AWS services are functioning. Total crash features by day tracks total number of crashes in the data set over time. Crashes added per day shows the number of new crashes added each day from the AWS service. Average reporting delay captures the difference in Date Added minus Crash Date to capture the timeliness of data reporting. A delay of less than 28 days is ideal for proper performance measure reporting.
If there are multiple days in a row (5+) where no new crash points are added, the pipeline should be checked for proper functioning.
This page shows the number of crashes occurring post-construction for the plotted date range to monitor over time. The 28-day delay is accounted for when each point is plotted daily.