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A simple framework for a complex problem? Predicting wildlife – vehicle collisions

View Article: PubMed Central - PubMed

ABSTRACT

Collisions of vehicles with wildlife kill and injure animals and are also a risk to vehicle occupants, but preventing these collisions is challenging. Surveys to identify problem areas are expensive and logistically difficult. Computer modeling has identified correlates of collisions, yet these can be difficult for managers to interpret in a way that will help them reduce collision risk. We introduce a novel method to predict collision risk by modeling hazard (presence and movement of vehicles) and exposure (animal presence) across geographic space. To estimate the hazard, we predict relative traffic volume and speed along road segments across southeastern Australia using regression models based on human demographic variables. We model exposure by predicting suitable habitat for our case study species (Eastern Grey Kangaroo Macropus giganteus) based on existing fauna survey records and geographic and climatic variables. Records of reported kangaroo–vehicle collisions are used to investigate how these factors collectively contribute to collision risk. The species occurrence (exposure) model generated plausible predictions across the study area, reducing the deviance by 30.4%. The vehicle (hazard) models explained 54.7% variance in the traffic volume data and 58.7% in the traffic speed data. Using these as predictors of collision risk explained 23.7% of the deviance in incidence of collisions. Discrimination ability of the model was good when predicting to an independent dataset. The research demonstrates that collision risks can be modeled across geographic space with a conceptual analytical framework using existing sources of data, reducing the need for expensive or time‐consuming field data collection. The framework is novel because it disentangles natural and anthropogenic effects on the likelihood of wildlife–vehicle collisions by representing hazard and exposure with separate, tunable submodels.

No MeSH data available.


Predicted relative traffic volume in number of vehicles per day per road segment in study area. Darker shades indicate higher predicted traffic volumes (mean: 4481; range: 274–60850).
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ece32306-fig-0003: Predicted relative traffic volume in number of vehicles per day per road segment in study area. Darker shades indicate higher predicted traffic volumes (mean: 4481; range: 274–60850).

Mentions: Average annual daily traffic (AADT) represents the sum of traffic traveling in both directions which pass a roadside observation point during a full year divided by 365 days for a given road segment. AADT volume is usually only available for major road segments, and we did not have data for most local, collector, and subarterial roads under municipal district control. We predicted volume estimates for all road segments in the study area with random forests regression (Breiman 2001). The dependent variable was 2013 AADT recorded by VicRoads on 3174 road segments. We included seven predictor variables (Table 1) that related to processes in traditional four‐step traffic demand modeling (trip generation, trip distribution, mode choice, and route assignment). All of the traffic model predictor variables were below a pairwise correlation threshold of 0.7 to reduce potential effects of multicollinearity. The traffic volume submodel used the log‐link function on the dependent variable (Table 2) due to the approximate log‐normal distribution of AADT. We predicted AADT to all road segments using the model fit (Fig. 3).


A simple framework for a complex problem? Predicting wildlife – vehicle collisions
Predicted relative traffic volume in number of vehicles per day per road segment in study area. Darker shades indicate higher predicted traffic volumes (mean: 4481; range: 274–60850).
© Copyright Policy - creativeCommonsBy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5016659&req=5

ece32306-fig-0003: Predicted relative traffic volume in number of vehicles per day per road segment in study area. Darker shades indicate higher predicted traffic volumes (mean: 4481; range: 274–60850).
Mentions: Average annual daily traffic (AADT) represents the sum of traffic traveling in both directions which pass a roadside observation point during a full year divided by 365 days for a given road segment. AADT volume is usually only available for major road segments, and we did not have data for most local, collector, and subarterial roads under municipal district control. We predicted volume estimates for all road segments in the study area with random forests regression (Breiman 2001). The dependent variable was 2013 AADT recorded by VicRoads on 3174 road segments. We included seven predictor variables (Table 1) that related to processes in traditional four‐step traffic demand modeling (trip generation, trip distribution, mode choice, and route assignment). All of the traffic model predictor variables were below a pairwise correlation threshold of 0.7 to reduce potential effects of multicollinearity. The traffic volume submodel used the log‐link function on the dependent variable (Table 2) due to the approximate log‐normal distribution of AADT. We predicted AADT to all road segments using the model fit (Fig. 3).

View Article: PubMed Central - PubMed

ABSTRACT

Collisions of vehicles with wildlife kill and injure animals and are also a risk to vehicle occupants, but preventing these collisions is challenging. Surveys to identify problem areas are expensive and logistically difficult. Computer modeling has identified correlates of collisions, yet these can be difficult for managers to interpret in a way that will help them reduce collision risk. We introduce a novel method to predict collision risk by modeling hazard (presence and movement of vehicles) and exposure (animal presence) across geographic space. To estimate the hazard, we predict relative traffic volume and speed along road segments across southeastern Australia using regression models based on human demographic variables. We model exposure by predicting suitable habitat for our case study species (Eastern Grey Kangaroo Macropus giganteus) based on existing fauna survey records and geographic and climatic variables. Records of reported kangaroo–vehicle collisions are used to investigate how these factors collectively contribute to collision risk. The species occurrence (exposure) model generated plausible predictions across the study area, reducing the deviance by 30.4%. The vehicle (hazard) models explained 54.7% variance in the traffic volume data and 58.7% in the traffic speed data. Using these as predictors of collision risk explained 23.7% of the deviance in incidence of collisions. Discrimination ability of the model was good when predicting to an independent dataset. The research demonstrates that collision risks can be modeled across geographic space with a conceptual analytical framework using existing sources of data, reducing the need for expensive or time‐consuming field data collection. The framework is novel because it disentangles natural and anthropogenic effects on the likelihood of wildlife–vehicle collisions by representing hazard and exposure with separate, tunable submodels.

No MeSH data available.