COM-1, pages 179-226 - ICOET

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We compared the reliability data to the recommended performance requirements that were obtained throughinterviews with three stakeholder groups (Huijser et al. 2009b).RESULTSThe percentage of false positives (i.e., a detection is reported by a system but there is no large animal present in thedetection zone) was relatively low for both systems (Magal Senstar: 0%; STS: 0.41%). However the percentage of falsenegatives (i.e., an animal is present in the detection zone but a system failed to detect it) differed substantially (MagalSenstar: 1.9%; STS: 16.8%). The percentage of intrusions (i.e., animal intrusions in the detection area) that weredetected also varied substantially (Magal Senstar 99.5%; STS: 88.6%). A comparison of the reliability data to therecommended performance requirements that were obtained through interviews with three stakeholder groups (Huijseret al. 2009b) showed that the Magal Senstar system met the recommended requirements, while the STS system didnot (Table 5).Table 5. The reliability of the two systems in relation to the recommended minimum norms. Thepercentage of intrusions detected is similar, though not exactly the same as the complement ofthe percentage of false negatives (see Huijser et al. 2009b).Manufacturer andsystem nameMagal Senstar(Perimitrax®)Meets falsepositives(yes/no)Meets falsenegatives(yes/no)Meets intrusionsdetected(yes/no)yes yes yes yesMeets overallrecommended norms(yes/no)STS (RADS III) yes no no noDISCUSSION AND CONCLUSIONThe results of the reliability tests showed that the percentage of false positives was low for both systems. Thepercentage of false negatives was higher than the recommended minimum norm for the STS system though. The STSsystem also did not meet the suggested minimum norm for detection intrusions in the detection zone. The MagalSenstar system met all of the recommended performance requirements for reliability. However, experiences withinstallation, operation and maintenance with this system along Hwy 160 near Durango, Colorado, showed that thissystem may still have problems with the integration of different system components (Huijser et al. 2011). Therobustness of this and other animal detection systems (see e.g. Huijser et al. 2009b) may have to be improved beforethe systems can be deployed on a large scale.BIOGRAPHICAL SKETCHESMarcel Huijser received his M.S. in population ecology (1992) and his Ph.D. in road ecology (2000) at WageningenUniversity in Wageningen, The Netherlands. He studied plant-herbivore interactions in wetlands for the Dutch Ministryof Transport, Public Works and Water Management (1992-1995), hedgehog traffic victims and mitigation strategies inan anthropogenic landscape for the Dutch Society for the Study and Conservation of Mammals (1995-1999), andmultifunctional land use issues on agricultural lands for the Research Institute for Animal Husbandry at WageningenUniversity and Research Centre (1999-2002). Currently Marcel works on wildlife-transportation issues for the WesternTransportation Institute at Montana State University (2002-present) and he is a member of the TransportationResearch Board (TRB) Committee on Ecology and Transportation.Tiffany Allen received a MSc in ecology at Montana State University in Bozeman, MT in 2011, a BSc in fish and wildlifemanagement from Montana State University in Bozeman, MT in 2006, and a BM in music theory from FurmanUniversity in Greenville, SC in 2004.Matt Blank is an assistant research professor at the Western Transportation Institute and the Department of CivilEngineering at Montana State University. Matt earned his Master of Science and Ph.D. in Civil Engineering at MontanaState University. He earned his Bachelor of Science in Geological Engineering at the University of Wisconsin-Madison.His research focuses on the interactions of roads and riparian corridors with an emphasis on aquatic connectivity.ICOET 2011 Proceedings 224 Session COM-1
Mark Greenwood is a tenured Associate Professor of Statistics in the Department of Mathematical Sciences atMontana State University in Bozeman, MT. He received a PhD in Statistics from the University of Wyoming in 2004. Hisresearch involves nonparametric and nonlinear statistical methods with applications in geosciences, ecology,neuroscience, and economics.Mr. Shaowei Wang was a Research Engineer at WTI through 2010, where he focused on systems and softwareengineering, data mining and statistical analysis for transportation safety and road weather management, GeographicInformation Systems (GIS), software, web, and multimedia development, and cost benefit analysis for engineeringapplications. He has extensive skills in system architecture design, prototyping, simulation, and modeling. Mr. Wangholds a Master's Degree in Industrial and Management Engineering from Montana State University. He is also amember of the International Council on Systems Engineering.Larry Hayden has more than 20 years’ experience as a Senior Design Engineer, specializing in telecommunications,radio frequency and microwave circuitry. He has a strong background in mechanical engineering issues, and excellenttroubleshooting skills. He has extensive experience in private industry product development, including eight years atBall Aerospace, where he developed components for telecommunications satellites. At the Western TransportationInstitute, he has applied his technical expertise to the development of communication solutions for transportationresearch facilities, special events, transportation safety and a virtual traffic management center. Currently, Mr. Haydenserves as a Research Associate at the Western Transportation Institute where he focuses on the analysis anddevelopment of telecommunications systems for transportation applications.Mr. Mohammad (Ashkan) Sharafsaleh is a Senior Research and Development Engineer with California PATH Programof UC Berkeley. He has multiple advanced degrees in engineering and has worked as a consultant in private sector aswell as the assistant City Traffic Engineer for the City of Berkeley prior to joining PATH in 2002. At PATH, he hasmanaged a variety of projects including Performance Measurement System (PeMS), ITS Decision Website, Constructionof PATH’s Intelligent Intersection Test-Bed, and Evaluation of Animal Warning System effectiveness. He has alsocontributed to a number of other PATH projects throughout the years. The examples of these projects includeIntersection Decision Support, Vehicle-Infrastructure Integration, and Cooperative Intersection Collision AvoidanceSystems. He has also vast experience dealing with cities, counties, and state institutions for deployment and fieldcollection efforts.ACKNOWLEDGEMENTSThe authors would like to thank the Colorado Department of Transportation and the California Department ofTransportation for initiating and supporting the projects on which this manuscript is based. The authors are particularlygrateful to Brian Allery, Tony Cady, Kevin Curry, Vanessa Henderson, Jeff Peterson, Bryan Roeder, and Mike McVaugh(all from the Colorado Department of Transportation), Alison Deans Michael (US Fish and Wildlife Service), Allen Graber,Amanda Kuenzi, and Jana Sterling (all from SWCA Environmental Consultants), Samir Siouti (Senstar Corporation), andLloyd Salsman for their help with installing the systems in the test-bed, Wicken’s Construction for their help with systeminstallation and modifications to the test bed, and Lethia Olson for supplying livestock.LITERATURE CITEDADM Alliance Nutrition Inc. 2011. The goat guide. Alliance Nutrition Inc., Quincy, Illinois, USA: Available from theInternet: URL: http://www.admani.com/AllianceGoat/Goat%20guide/S9761%20Goat%20Guide%202010.pdfGagnon, J.W., N.L. Dodd, S.C. Sprague, K. Ogren and R.E. Schweinsburg. 2010. Preacher Canyon Wildlife Fence andCrosswalk Enhancement Project Evaluation. State Route 260. Final Report - Project JPA 04-088. Arizona Game andFish Department, Phoenix, AZ, USA.Gordon, K.M. and S.H. Anderson. 2002. Motorist response to a deer-sensing warning system in western Wyoming. In2003 Proceedings of the International Conference on Ecology and Transportation. 24-28 September 2001.Keystone, CO, USA. pp. 549-558.Gordon, K.M., M.C. McKinstry and S.H. Anderson. 2004. Motorist response to a deer sensing warning system. WildlifeSociety Bulletin 32: 565-573.Hammond, C. and M.G. Wade. 2004. Deer avoidance: the assessment of real world enhanced deer signage in a virtualenvironment. Final Report. Minnesota Department of Transportation. St. Paul, Minnesota, USA. Available from theinternet. URL: http://www.lrrb.gen.mn.us/pdf/200413.pdf.Reducing Wildlife-Vehicle Collisions 225 ICOET 2011 Proceedings
Page 1 and 2: Construction, Operations and Mainte
Page 3 and 4: METHODSCarcass removal logs were co
Page 5 and 6: US 6 Big Game WVCs2005-2010160140#
Page 7 and 8: Figure 6. US 6 bridge underpass at
Page 9 and 10: crossings on US 6 include: moose, b
Page 11 and 12: making wildlife mitigation recommen
Page 13 and 14: Record of Decision: US 6 from I-15
Page 15 and 16: demonstrated underpasses and extend
Page 17 and 18: included in this project represent
Page 19 and 20: RESULTSPass-wide Data SummaryBetwee
Page 21 and 22: Road killWithin the mitigation zone
Page 23 and 24: After the mitigation fencing was in
Page 25 and 26: cross at the fence ends, as road ki
Page 27 and 28: SignageAny application of temporary
Page 29 and 30: Proctor, M. F., C. Servheen, S. D.
Page 31 and 32: Figure 1. Approximate location of g
Page 33 and 34: Table 1. Number of mule deer that m
Page 35 and 36: Figure 4. Total number of mule deer
Page 37 and 38: Mule Deer-Vehicle CollisionsBefore
Page 39 and 40: Plumb, R. E., K. M. Gordon, and S.
Page 41 and 42: Animal detection systems detect lar
Page 43 and 44: Animal Detection Systems and Data R
Page 45: Table 4. Body size and weight of th

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