Spatiotemporal analysis on animal-vehicle collisions

Collaborators: Dr. Giorgos Mountrakis, Kari Gunson

Spatiotemporal MVCs
Moose-Vehicle Collisions in Space-Time

Funders: Humane Society, SUNY ESF Graduate Assistantship

Motivation: Moose-vehicle collisions (MVCs) pose a serious safety and environmental concern in many regions of Europe and North America. For example, in the state of Vermont one-third of all reported MVCs resulted in motorist injury or fatality while collisions have increased from two in 1982 to 164 in 2002. We studied varying spatiotemporal patterns of wildlife collisions along roads to assist transportation planners in identifying optimal mitigation strategies along specific roads, such as deciding on location and spatial length for permanent and expensive measures (e.g. crossing structures and associated fencing) versus less permanent and inexpensive structures (e.g. wildlife signage and reduced speed limits).

Methodology: Our work used a MVC dataset from 1983 to 1999 in the Northeastern Highlands of Vermont (four major roads) to perform space, time and spatiotemporal analyses and guide future mitigation strategies. An adapted kernel density estimator was implemented for exploratory analyses to detect high density collision hotspots on roads. We also developed an adapted Ripley’s K-function to test the hypothesis that MVCs clustering occurred at multiple scales in space, in time and in space-time combined.

Findings: The kernel in space showed seven major density peaks which varied in magnitude and spread between roads. The kernel estimator in time for all roads showed an exponentially increasing trend with annual periodicity and a seasonal cyclic component, where the majority of collisions occurred from May to October. Spatiotemporal kernel estimation exhibited discontinuous density hotspots in time and space suggesting changing animal movement patterns across roads. Statistically significant spatial clustering was evident on all roads at spatial scales from 2 to 10 kilometers. A more consistent clustering in time occurred on all roads at a scale distance of 5 years. Similar to the kernel estimation, annual periodicity was also evident. Positive space-time clustering was present at small spatial (5 km) and temporal scales (2 years) indicating that where MVCs occur is also influenced by when they occur. In retrospect, using multiple road lengths, and the combined kernel estimation and Ripley’s K-function in time and space, provided a powerful methodology to study varying spatiotemporal patterns of wildlife collisions along roads.

Relevant papers: G. Mountrakis, K. Gunson (2009). Multi-scale spatiotemporal analyses of moose-vehicle collisions: A case study in northern Vermont. International Journal of Geographical Information Science, 23(11):1389-1412.

K. Gunson, G. Mountrakis, L. Quackenbush (2011). Spatial wildlife-vehicle collision models: A review of current work and their application to transportation mitigation projects. Journal of Environmental Management, 92(4):1074-1082.