Examine individual changes

'== Deer–Vehicle Collision Risk Calculator == Deer–vehicle collisions (DVCs) are a tangible roadway risk that varies by **state**, **season**, and **time of day**. Insurance-claim statistics indicate a national annual risk near '''1 in 128''' for hitting an animal (most commonly deer) in {{lc:{{CURRENTYEAR}}}}, with the highest risks in certain Appalachian and Upper Midwest states. Autumn evenings (especially **October–November**) are peak season. This calculator lets you estimate your risk at three levels: * '''Annual''' (per driver per year, by state) * '''Monthly''' (seasonality by month) * '''Trip-level''' (for a specific drive, by miles, road context, time of day, and corridor mitigation) === Your Inputs === Choose conditions below. (Defaults reflect typical U.S. conditions and 14,000 miles/year.) <RiskParameter>distance_miles=14000</RiskParameter> Select your '''state''' (annual odds per driver): <DropDown title="State / Region" table="DVC_Annual_Odds_By_State_2024_2025" default-index=0></DropDown> Seasonality strongly concentrates risk in autumn; pick a '''month''' to weight annual odds: <DropDown title="State / Region" table="DVC_Annual_Odds_By_State_2022_2023" ...> Driving in darkness sharply raises DVC risk: <DropDown title="Time of Day" table="DVC_TimeOfDay_Options" default-index=0></DropDown> Pick your '''road context''' (per-mile baseline differs by rural/urban): <DropDown title="Road Context" table="DVC_PerMile_RoadContext" default-index=0></DropDown> Choose a '''trip length''': <DropDown title="Trip Distance" table="DVC_Trip_Length_Options" default-index=3></DropDown> If your corridor has wildlife fencing/crossings, apply a reduction: <DropDown title="Mitigation on Corridor" table="DVC_Mitigation_Options" default-index=0></DropDown> === Your Results === <RiskDisplay model="DVC_Annual_Per_Driver"></RiskDisplay> <RiskDisplay model="DVC_Monthly_Per_Driver"></RiskDisplay> ''Note: Monthly risk scales your selected state’s annual odds by the selected month’s share of annual deer crashes (using a well-documented Wisconsin distribution as a general proxy).'' <RiskDisplay model="DVC_TripLevel_Per_Drive"></RiskDisplay> ---- See [[Deer–Vehicle_Collisions/Data|the data page]] for sources and details of each calculation. Generated by [https://openai.com/ ChatGPT-5] == Data and Parameters == <riskdata table="DVC_Annual_Odds_By_State_2024_2025" columns="RegionLabel|DVC_State_Odds_1_in|DVC_State_Odds_DataPeriod"> United States (average)|1 in 139|Jul 2024–Jun 2025 West Virginia|1 in 40|Jul 2024–Jun 2025 Montana|1 in 53|Jul 2024–Jun 2025 Wisconsin|1 in 60|Jul 2024–Jun 2025 Pennsylvania|1 in 59|Jul 2024–Jun 2025 Michigan|1 in 61|Jul 2024–Jun 2025 North Dakota|1 in 82|Jul 2024–Jun 2025 South Dakota|1 in 69|Jul 2024–Jun 2025 Virginia|1 in 78|Jul 2024–Jun 2025 Missouri|1 in 97|Jul 2024–Jun 2025 Texas|1 in 222|Jul 2024–Jun 2025 Nevada|1 in 770|Jul 2024–Jun 2025 Hawaii|1 in 710|Jul 2024–Jun 2025 </riskdata> ''Annual per-driver odds of an animal-collision claim (deer are the top animal), period shown per row.'' Representative states are listed to keep the UI compact; the model will still work nationally using the U.S. average when a state isn’t listed. * [https://newsroom.statefarm.com/new-state-farm-data-reveals-fewer-animal-collisions-but-autumn-months-remain-most-dangerous/ State Farm newsroom (Sep 16, 2025): new animal-collision odds incl. 1 in 139 U.S. average for 2024–2025] * [https://www.statefarm.com/simple-insights/auto-and-vehicles/how-likely-are-you-to-have-an-animal-collision State Farm explainer: national odds and autumn peak] * [https://www.iii.org/fact-statistic/facts-statistics-deer-vehicle-collisions Insurance Information Institute: Deer-vehicle collisions (context, confirms State Farm counts/periods)] <riskdata table="DVC_Monthly_Wisconsin_2023" columns="SeasonMonthLabel|DVC_WI_Monthly_Count_2023|DVC_WI_Monthly_Context"> January|1033|Wisconsin police-reported deer crashes (calendar year 2023) February|881|Wisconsin police-reported deer crashes (calendar year 2023) March|904|Wisconsin police-reported deer crashes (calendar year 2023) April|857|Wisconsin police-reported deer crashes (calendar year 2023) May|1575|Wisconsin police-reported deer crashes (calendar year 2023) June|1543|Wisconsin police-reported deer crashes (calendar year 2023) July|828|Wisconsin police-reported deer crashes (calendar year 2023) August|738|Wisconsin police-reported deer crashes (calendar year 2023) September|925|Wisconsin police-reported deer crashes (calendar year 2023) October|2343|Wisconsin police-reported deer crashes (calendar year 2023) November|3218|Wisconsin police-reported deer crashes (calendar year 2023) December|1308|Wisconsin police-reported deer crashes (calendar year 2023) </riskdata> ''Monthly distribution (counts) for Wisconsin, 2023.'' Used to weight annual odds into a month-specific probability (November and October are the peak months; a May–June secondary bump is also visible). * [https://wisconsindot.gov/Documents/safety/education/crash-data/2023-deer-fact-sheet.pdf Wisconsin DOT: 2023 Deer-Related Crashes Fact Sheet (monthly counts chart)] <riskdata table="DVC_TimeOfDay_Options" columns="TimeOfDayOptionLabel|TimeOfDay_Relative_Multiplier|TimeOfDay_Notes"> Daylight (baseline)|1|Baseline risk level Night (sunset→sunrise)|3|Most DVCs occur after dark; conservative whole-night factor Peak window (~2 hours after sunset)|14|≈14× vs. two hours before sunset (research finding) </riskdata> ''Relative multipliers for time of day.'' The ~14× factor applies to the short peak window; using 3× for general night driving is a more conservative whole-trip assumption. * [https://www.washington.edu/news/2022/11/02/deer-vehicle-dst/ Univ. of Washington summary of Current Biology study (1994–2021 dataset; 14× finding)] * [https://pubmed.ncbi.nlm.nih.gov/36327981/ Current Biology (2022): deer–vehicle collisions and clock shifts] <riskdata table="DVC_PerMile_RoadContext" columns="RoadContextLabel|DVC_Rate_per100M_VehicleMiles|DVC_Rate_Context"> Overall (Kentucky baseline)|7|Police-reported DVCs per 100M VMT (1987–1989) Rural roads|11|Police-reported DVCs per 100M VMT (1987–1989) Urban roads|2|Police-reported DVCs per 100M VMT (1987–1989) </riskdata> ''Per-mile baseline rates by road context.'' Used for trip-level risk; modern/local conditions vary, so treat as illustrative until calibrated with newer local VMT-normalized data. * [https://www.cdc.gov/mmwr/preview/mmwrhtml/00015469.htm CDC MMWR (1991): Injuries from Motor-Vehicle Collisions with Deer — Kentucky, 1987–1989] <riskdata table="DVC_Trip_Length_Options" columns="TripLengthLabel|Trip_Miles"> Short errand (5 mi)|5 Commute (10 mi)|10 Suburban cross-town (20 mi)|20 Highway leg (50 mi)|50 Road trip segment (100 mi)|100 </riskdata> ''Trip distance options (miles).'' Scales per-mile risk to a single drive. <riskdata table="DVC_Mitigation_Options" columns="MitigationLabel|DVC_Reduction_Percent|DVC_Mitigation_Context"> None / no special mitigation|0|Reference Wildlife fencing + crossing structures|80|Synthesis; ungulates incl. deer Wildlife fencing (typical median)|54|Global meta-analysis median All mitigation types (overall average)|40|Global meta-analysis average </riskdata> ''Observed reductions from corridor mitigations.'' Apply as a percent reduction to expected collisions on the mitigated segment. * [https://www.fhwa.dot.gov/publications/research/safety/08034/08.cfm FHWA (2008): Wildlife–Vehicle Collision Reduction Study] * [https://pmc.ncbi.nlm.nih.gov/articles/PMC5117745/ PLoS One (2016): Meta-analysis of road-kill mitigation effectiveness] * [https://www.sciencedirect.com/science/article/abs/pii/S0006320716300350 Biological Conservation (2016): Fencing & crossing structures effectiveness] == Risk Models == <riskmodel name="DVC_Annual_Per_Driver"> Your '''deer–vehicle collision''' risk is {{One_In_X|{{#expr: 1 / {DVC_State_Odds_1_in} }} }} per '''year'''. ''What this shows'': Per-driver annual odds from insurance claims for the selected state/region (or U.S. average). </riskmodel> <riskmodel name="DVC_Monthly_Per_Driver"> Your '''deer–vehicle collision''' risk is {{One_In_X|{{#expr: (1 / {DVC_State_Odds_1_in}) * ({DVC_WI_Monthly_Count_2023} / (1033+881+904+857+1575+1543+828+738+925+2343+3218+1308)) }} }} per '''month'''. ''How computed'': Annual probability (''1 ÷ {DVC_State_Odds_1_in}'') scaled by the chosen month’s share of annual deer crashes using Wisconsin-2023 distribution as a general U.S. proxy. </riskmodel> <riskmodel name="DVC_TripLevel_Per_Drive"> Your '''deer–vehicle collision''' risk is {{One_In_X|{{#expr: ({DVC_Rate_per100M_VehicleMiles} / 100000000) * {Trip_Miles} * (({DVC_WI_Monthly_Count_2023} * 12) / (1033+881+904+857+1575+1543+828+738+925+2343+3218+1308)) * {TimeOfDay_Relative_Multiplier} * (1 - ({DVC_Reduction_Percent} / 100)) }} }} per '''drive'''. ''Explanation'': # '''Per-mile baseline''' from selected road context: ''{DVC_Rate_per100M_VehicleMiles} ÷ 100,000,000''. # Scale by '''trip length''' (''× {Trip_Miles}''). # Apply '''seasonal factor''': month’s share relative to an average month (''× ({DVC_WI_Monthly_Count_2023} × 12 ÷ annual sum)''). # Apply '''time-of-day multiplier''' (''× {TimeOfDay_Relative_Multiplier}''). # Apply any '''mitigation reduction''' (''× (1 − {DVC_Reduction_Percent}/100)''). ''Caveat'': Baseline rates are from Kentucky (historic) and seasonality from Wisconsin; adjust with local VMT-normalized data when available. </riskmodel> ---- Data and risk models are used on the [[Deer–Vehicle_Collisions|main page]]. ''Initially created by GPT-5 Thinking'''