COMPARISON AND EVALUATION OF ROADSIDE ANIMAL SENSING AND DRIVER WARNING SYSTEMS In Response to 2020 NDOT Problem Statement 20Q2E101

Hao Xu (PI), Ph.D., P.E., Associate Professor (775) 784-6909 Department of Civil and Environmental Engineering University of Nevada, Reno [email protected]

Fraser Shilling, PhD., Co-Director Road Ecology Center Department of Environmental Science and Policy University of California, Davis 530-752-7859 [email protected]

Jeff Gagnon, Regional Supervisor Wildlife Contracts Branch Connectivity Group Arizona Game and Fish Department Phoenix, AZ 86056 928-814-8925 [email protected]

Tim Hazlehurst, President CrossTek EB, LLC Seattle, Washington 330-414-1995 [email protected]

20Q2E101: Comparison and Evaluation of Roadside Animal Sensing and Driver Warning Systems

1. PROBLEM DESCRIPTION Animals regularly enter and attempt to traverse roads, putting themselves and vehicle occupants in danger. This can cause drivers to collide with animals or change direction abruptly to try and avoid a collision, resulting in property damage and injury or death. In areas where fencing or crossing structures are not present or feasible, roadside animal detection systems (RADS) can alert drivers and sometimes the animals, to the presence of the other and reduce the risk of collision. There is an increasing array of technologies that detect animals in the roadside, classify animal type, map movement trajectory, and warn drivers. A comprehensive evaluation and comparison of currently available sensing-technologies for RADS is needed for the Nevada Department of Transportation (NDOT) and other states to select appropriate technologies and reduce animal-vehicle-collisions for driver safety and wildlife conservation. 2. BACKGROUND SUMMARY NDOT and partner agencies have been successful in building wildlife crossing structures and highway fences to reduce wildlife-vehicle collisions. However, for areas with limited NDOT right-of-way or requiring high accessibility, crossing structures and fences are not feasible. The USA Parkway (SR 439) through the Industrial Center is one of these situations. RADS are considered to detect animals nearby or on roads and warn motorists to reduce driver speed and increase attentiveness. Animal detection technologies are normally classified into two groups – break-the-beam sensors and area coverage sensors. Area coverage sensors detect animals within the detection zone of the sensor, but break-the-beam only “see” animals crossing a beam. A major interest in animal sensing and warning systems is area coverage sensing technologies, including video/still camera, Radar, thermal camera, and advanced Light Detection and Range (LiDAR) sensors. Several studies have compared options for animal detection sensors in controlled environments, but these are dated and there is still a lack of widely-agreed-upon sensing solution. Video and still cameras with infrared illumination and real-time data transmission provide visual evidence of animal presence. They have promising detection and tracking capabilities when combined with cutting-edge AI image processing algorithms and hardware. Radar sensors provide longer detection distance than other options, but some radar systems have resulted in high percentages of false positives (sensing systems report animal crossing when there is actually no animal). New radar sensors and AI data processing have been developed for higher roadside detection accuracy, so a further evaluation of the latest radar sensing for animal crossings is needed. The advancements in LiDAR sensors, such as 360-degree 3D LiDAR and image-grade LiDAR sensors, are also promising solutions for wildlife detection, providing rapid detection of large wildlife, vehicles, and pedestrians. However, accurate object classification decreases with distance, which could impact detection and driver warning. Thermal sensors have been used successfully in Arizona at an at-grade crossing where fencing guides animals to a discrete location, but the use of this technology has not been evaluated over longer stretches of road without fencing. While these sensors are also used for driver-assistance programs and autonomous vehicles, the requirements for roadside deployment and data processing are quite different from onboard sensing systems.

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There is no systematic comparison of these latest area coverage sensing technologies in a real- world environment with various traffic and weather conditions. A comparison of these newer and advanced sensing technologies will provide reference information to traffic and animal agencies for a cost-effective solution and reduction in animal-vehicle collisions. Besides the sensing and classification functions, RADS also need to warn drivers of animal-vehicle collision risks when animals approach or step on roads. Two major warning methodologies are real-time messages to connected and autonomous vehicles (CAVs) and automatic wildlife- crossing warning systems. When different sensing technologies provide various data output, testing appropriate communication for the sensors is critical to RADS efficiency. 3. PROPOSED RESEARCH AND FIELD DEPLOYMENT Our project team includes some of the foremost investigators in the field of animal detection and movement. The team proposes to meet NDOT’s needs by deploying mature instrument technologies side-by-side, the majority of which will be available without cost to the project. Our technologies (instruments and processing) are mature and deployable on the roadside. At the same time, technologies are changing fast enough, including price points, that we also propose to evaluate emerging technologies selectively. This project will be a comparative study in real-world field conditions to compare the ability of multiple sensor types (LiDAR, Thermal, Camera/Video, Radar) for rapid detection and correct classification of animals in varied roadside environments. This project will also develop a deployable combination of one or more sensor types alongside roadways with varying conditions, to determine the most cost-effective stand-alone or combination of sensors. The project team will use an Intelligent Transportation System (ITS) trailer, shown in Figure 1, as the platform for deployment of the integrated animal sensing and warning system. The trailer has been borrowed from NDOT District II for current UNR research on roadside sensing. The trailer with solar panel and wind turbine power devices can provide 24/7 power supply, house edge processing units, remotely control roadside warning signals through radio communication, and support remote system monitoring and operation through LTE connection. A CCTV camera on the top of the trailer can be remotely accessed and operated for equipment security, or to record animal presence. The test system will be placed for one year each at 1) USA Parkway, where wild horses cross the roadway in several places, 2) US 395 between Stead Blvd and White Lake Parkway, the #2 priority site statewide for NDOT for deer-vehicle collisions. NDOT can modify the choice of the second site during the project to meet other needs. To evaluate performance of roadside animal sensing and driver-warning technologies, the project team will integrate the available area-coverage wildlife sensing technologies on the ITS trailer. Data output from each sensor will be processed, validated, and compared in terms of cost, power requirements, installation requirements, communication requirements, range, sensitivity, accuracy, and reliability in different weather conditions. The project teams’ expertise, ready-to- use hardware and software, and long-term field deployment experience in wildlife and traffic detection systems are the key to this project’s success. LiDAR solution - Xu with UNR has been performing research on multi-modal traffic monitoring and tracking with roadside sensing systems for more than five years, especially advanced LiDAR sensing technologies. Software for logging and processing roadside high- resolution LiDAR data have been developed and used at seven sites with permanent LiDAR

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installation, and multiple portable platforms in northern and southern Nevada. The software converts LiDAR point-clouds to geolocated trajectories of all traffic (including vehicles, pedestrians, animals, and other road users). Figure 1 includes an example of half-hour geolocated vehicles (green dots) and horse trajectories (red dots) from the LiDAR ITS trailer at USA Pkwy. Presence and crossing of horses were confirmed using motion-triggered cameras (Shilling, UC Davis). Accuracy of presence, volume, speed, and location-detection by LiDAR has been validated in previous research and published in journals and conference papers (Xu et al. 2018, Wu et al. 2018). Besides detection of animals nearby and on roads, vehicle trajectories from LiDAR sensors can also report whether and how much a roadside warning reduces traffic speeds.

Figure 1. NDOT ITS trailer equipped with equipment of power supply, sensing, edge computation, and wireless communication by UNR; Sample all-traffic trajectories from roadside LiDAR at USA Pkwy (half- hour data of 11/18/2019 2:00-2:30 PM). Green dots represent vehicles and red dots horses. Velodyne VLP-32c sensors scan the surrounding environment with 32 laser beams, and 200- meter detection radius, and they are used at most UNR’s LiDAR deployment sites in urban and rural areas. A VLP-32c sensor will be integrated onto the ITS trailer for the two-year field deployment. UNR also owns low-cost Velodyne VLP-16 LiDAR sensors that scan with 16 laser beams, 200-meter detection radius, and much lower cost than VLP-32 ($2,000 per unit VLP-16 V.S., $35,000 per unit VLP-32c, based on the most-recent market price). VLP-16 will also be installed on the ITS trailer for at least several weeks. Another new LiDAR sensor that UNR will contribute to the proposed project is the Innovusion Cheetah image-level LiDAR. The Cheetah LiDAR provides image-level cloud-point resolution, with a 300-line, 40-degree vertical field of view, a maximum 300-meter detection distance in a 100-degree horizontal range, and the highest point density of existing portable LiDAR sensors. The image-level LiDAR is a new laser-scan technology. It has not been tested for roadside animal sensing so will also be installed on the trailer platform for at least several weeks, using UNR’s current software and road-side processing units. Camera/video solution - Cameras/video systems provide validation information for sensor comparison and are sensors in their own right. Detecting and identifying/classifying objects in moving imagery (video, CCTV feed) offers an opportunity to both map an object’s trajectory and

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to determine if it is of concern from a driver-safety point-of-view. There are low-cost, commercial UltraHD (4K) outdoor CCTV cameras with 100m (day or night) detection ranges, motion detection, raw or on-board image processing, and DC power. There are also existing machine-learning-based approaches (YOLO, Faster-R-CNN, SSD) that can use video streams to allow very rapid (<<1 second) detection and classification of moving objects, including people, vehicles, and animals. These are based on three steps, the first two of which UC Davis employs in its ImageID tool (https://wildlifeobserver.net/ImageID): 1) the creation of a bounding box around moving objects of interest, 2) classification of the object as vehicle, human, animal, and possibly animal type, and 3) mapping trajectories of moving objects of interest relative to the roadway.

Because of the rapidly-changing field of AI-classification using camera/video systems, we will also carry out desktop evaluation of commercial solutions, such as the AWS DeepLens video camera (https://aws.amazon.com/deeplens/) combined with the AWS Snowcone (https://aws.amazon.com/snowcone/) edge computing solution. This combination provides on- board image processing, including artificial-intelligence-based object detection and classification and transmission of decisions/alerts regarding the objects. It also provides massive local processing power and storage. co-PI Shilling will discuss options with the developer of the system, Gopi Prashanth Gopal, Director of Technology at Amazon.

Thermal Solution - Thermal sensors passively detect thermal energy radiated from all objects in the field of view of the camera, including live animals from the very smallest mammals to the very largest animals typically observed on or near roadways. High-quality thermal sensors detect small differences in temperature and when this data is combined with analytics developed specifically to detect wildlife and livestock near roadways, very accurate classification, location, and direction of travel can be determined. Thermal sensors are effective in snow, rain, wind and extreme temperature conditions. Although certain conditions can reduce the distance of the detection field, this is typically accounted for in the deployment layout. Thermal sensors have been used successfully as a RADS sensing component in Arizona for more than a decade at the “elk crosswalk” in Arizona. co-PI Gagnon has worked closely with co-PI Hazlehurst to evaluate the effectiveness of the system in Arizona and published the results from this long-term evaluation (Gagnon 2019). In 2019 and 2020, co-PI Hazlehurst worked closely with FLIR to upgrade and develop the sensing hardware and software to fine-tune the accuracy of the system to the 95% accuracy requirement set by AGFD and ADOT. It is this validated system that will be used for the comparison of sensing technologies. One drawback of the AZ thermal RADS is the coverage range of the thermal sensors (150 feet); however, this distance can be improved 2-3- fold using lenses to narrow the field, concentrating effort in a smaller area. Radar Solution - Radar sensing is one of the more recent technologies used in a real-world RADS setting. Radar transmits a focused pulse of microwave at an object and the portion of this beam reflects back and is analyzed by the radar, providing enough information about the object to determine, distance, size, speed, direction of travel, and reflectivity of the object. This technology can identify and track movements of wildlife, among other natural and anthropogenic features that may be present. Although one of the more expensive sensing technologies we will evaluate on a per sensor cost basis, radar has the distinct advantage of long-range detection (e.g. up to 2.5 miles one both sides of road with one sensor) over other sensing technologies we will evaluate in this study. co-PI Hazlehurst will be working with his support team, including the

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FLIR Radar Surveillance group and the engineers responsible for designing and monitoring the successfully deployed radar system operating effectively today at two locations in British Columbia (Figure 2).

Figure 2. Photo of a thermal sensor, warning sign, and example of thermal images collected by the RADS along SR 260 in AZ. Photo of a radar sensor, warning sign, and vehicle slowing for bighorn sheep detected by radar along a roadway in British Columbia (right) Real-time warning to drivers/vehicles – Xu with UNR has implemented a roadside software program that broadcasts roadside sensing information to connected vehicles for Cohda DSRC roadside and onboard units. Cohda is one of major DSRC vendors for connected-vehicle systems in the U.S.). An in-vehicle Android application was also developed to visualize roadside information on a map, and it was implemented for testing roadside-LiDAR trajectory data to connected drivers. With minor adjustment, the existing roadside software, onboard application, and the available Cohda DSRC units can be used to broadcast DSRC safety messages in this project. UNR implemented, deployed, and is maintaining a LiDAR-triggered rectangular rapid flashing beacon (RRFB) for a pedestrian crosswalk in Henderson, Nevada. When a roadside LiDAR detects any pedestrians about to cross the road, the system automatically triggers RRFB flashing. The system used a Tapco radio communication device for the remote control, and the device can be directly used to trigger wildlife-crossing warning signs remotely.

Evaluation of system reliability and performance - There are several key factors in determining if a RADS is suitable for advancement to pilot or full deployment on the roadside. We will test network performance in terms of “CARS” criteria: Completeness (systems correctly detects and censuses all detectable objects), Accuracy (systems correctly classifies objects, few false positives), Reliability (systems powered and communicate effectively 99%-100% of time), and Sensitivity (systems identify all relevant, moving objects in the detection zone, few false negatives). The accuracy of RADS is directly linked to the successful reduction of speed and increase in awareness required by motorists to either completely avoid or reduce the severity of collisions with wildlife. When a RADS does not activate warning signs when wildlife enter the detection zone, this is referred to as a false negative. In contrast, when a RADS activates without wildlife present, this is referred to as a false positive. Minimizing false positives and negatives prior to deployment of RADS will help ensure that the system will be effective when used in a real-world

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situation. Huijser et al. (2009) surveyed three stakeholder groups regarding RADS leading to the recommendation that animal detection technology detects at least 91% of large animals that approach the roadway and fewer than 10% false positives. To determine the accuracy of each type of technology, we will use a combination of visual inspection of the data output, time-lapse surveillance, and automated cross-referencing of outputs from the various technologies. We will conduct a baseline comparison of the rate of false negatives and false positives and of the sensing technologies at different distances (0-100 ft, 100- 500 ft, 500-1000ft, and >1000ft), in various weather conditions (rain, snow, and dust) and with large and small species, to determine the strengths and limitations of each. Specific Tasks Task 1 - Project management and communication: The research team will work with the NDOT project manager, related NDOT divisions and NDOT District II, to ensure that the scope of work and deliverables meet the NDOT’s needs, and for NDOT ITS trailer deployment and NDOT temporary right-of-way permit application. Team members will participate in a kickoff meeting (in-person or virtual) with NDOT to discuss agency priorities and detail the approach being considered and will develop project progress reports and contribute to quarterly project meetings led by UNR. Xu will take a lead of the project management and communication. Deliverables: 1) Kickoff meeting and quarterly project progress meetings, 2) Quarterly reports to the NDOT Research Office Task 2 – Implementation of the roadside animal-sensing-warning system and controlled test: The roadside sensing ITS trailer will be equipped with LiDAR, thermal sensors, camera/video systems, radar sensing, and data processing units. All team members will assist with design and implementation of the roadside sensing systems. Xu will deploy at least one 32- channel LiDAR unit and one image-quality LiDAR unit, with associated edge-computing processing. Shilling will be responsible for the CCTV/video unit and on-site processing solution. Hazelhurst and Gagnon will deploy one radar unit and one custom FLIR thermal sensing unit and on-site data processing solution. The team will develop protocols for testing network performance in terms of “CARS”: Completeness (systems correctly censuses all objects), Accuracy (systems accurately detect and classify objects, few false positives), Reliability (systems powered and communicate effectively 99%-100% of time). And Sensitivity (systems identify all relevant, moving objects in detection zones, few false negatives) Deliverables: 1) Design and demonstration of the roadside platform, 2) Protocol for testing sensing network performance Task 3 – Deployment and maintenance of the ITS Trailer: The research team will deploy the ITS trailer at USA Parkway, for one-year and then for an additional year at US 395. Initial deployment and redeployment will include setup of the four sensing technologies (Video/camera, LiDAR, Radar, and Thermal) and training of each sensing technology to the site if necessary before direct comparison of the technologies. Additional less expensive sensors will be considered and tested for shorter periods at both sites. Maintenance of the sites will include checking on power and batteries, cleaning equipment, data collection, and vegetation management. Initial setup and redeployment will require the presence of all PIs and required

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staff, including engineers. UNR Staff will conduct regular maintenance following training on each technology under the guidance of the co-PIs. Task 4 – Sensor data processing, validation, comparison, and integration: The project team will evaluate the capability and accuracy of sensing technologies to detect, identify, and track wildlife/livestock approaching and crossing highways. Animal type and movement data collected using the four sensing technologies will be validated through a combination of manual and automated review. The manual review will include a review of a sub-sample of 24-hour video surveillance footage collected during the comparison of the sensing technologies. The initial manual review will use the team members from AGFD and UC Davis. PIs will determine the need, if any, to make additional adjustments to improve the technologies, and additional validations will occur. Once the team is comfortable with the accuracy of the system, they will rely on the cross-referencing of the sensing technologies for future validation efforts. Once each sensing technology is validated independently, they will be compared at the USA Parkway site using CARS (Task 2). The team will collectively determine which individual or collection (e.g., pair) of sensors provides the most robust solution for RADS, including consideration of effectiveness of animal detection, power needs, cost, and complexity of further deployment (e.g., by NDOT). We will then use edge-computer processing on-site to pair/integrate the signals from these sensor(s) into one information stream, or signal, to inform driver warning. UNR will manage the raw data collected from the field sensors and share the data with NDOT and the project team. Deliverables: 1) Raw data collected by multiple sensors at each proposed study site, 2) Trajectories extracted from roadside sensor data collected at each site, 3) Evaluation of accuracy of roadside trajectories Task 5 - Proof-of-concept of connecting sensing platforms to driver warning: The research team will extend previous and current research for a comprehensive proof-of-concept study to further test communication with connected autonomous vehicles and driver warning signs:

• Communication among platforms, when more than one is present, • Automatic wildlife warning signal triggered by roadside sensors when wildlife/livestock animals crossing highways are detected • Connected-vehicle applications to warn drivers of animal crossing roads by broadcasting roadside sensor-mapped trajectories to vehicles equipped with DSRC onboard units Deliverables: Case study reports of connectivity and communication. Task 6 - Development of roadside sensing deployment guidance and NDOT implementation plan: Based on the research described in previous tasks, the project team will summarize the deployment experience and learning to develop guidance material for roadside sensing platform deployment and installation for NDOT, and other traffic agencies. The guidance will include suggested sensor features and combinations of sensor types for roadside animal-detection systems under various scenarios, positional specifications for individual and network of sensor platforms, and the expected performance of single/multiple sensor systems. The guidance will recommend an implementation plan for roadside animal sensing in Nevada that includes estimation of costs and timelines. Deliverables: Roadside Animal Detection System Deployment Guidance

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Task 7 – Final report: A final report will be prepared to document all the major findings and efforts related to the above tasks and will be submitted to the NDOT manager for review. The report will include comparisons of sensor-specific performance, coverage area, sensor and platform costs/100m roadside, power needs, and other support/maintenance requirements. NDOT’s comments on the draft report will be addressed. The revised project report and a comment-response document will be submitted to NDOT. Deliverables: Final project report 4. URGENCY AND ANTICIPATED BENEFITS Animal-vehicle-collisions (AVC) have increased nationally by approximately 50 percent, from less than 200,000 per year in 1990 to a high of approximately 300,000 per year in 2004, comprising approximately 5% of all motor vehicle collisions nationally (Huijser 2017). The estimated total annual cost associated with AVC, based on available data, is $8,388,000,000. From May 2015 through May 2016, 399 AVC were reported in Nevada, including one human fatality and 69 injuries; the estimated cost is about $6,000,000 in the one year (Average Economic Cost by Injury Severity or Crash, 2018). Many of these AVCs occur in hotspots of collisions (high density and/or statistically-significant clusters), suggesting that deploying mitigation in these places could reduce collisions. The price point of RADS is nearing that of physical fencing, while still permitting animal crossing of the road surface. If the RADS and driver-warning system described here was successfully deployed and prevented 10% animal- vehicle crashes ($600,000) each year, it could pay for itself in a relatively short time period. 5. IMPLEMENTATION PLAN The tasks and stages (include an estimate of costs beyond the proposed research) needed for full implementation of research results. 1. Collaboration between federal, state, and local stakeholders to define short-term and long-term objectives of roadside LiDAR sensing systems 2. Prioritize highway corridors for installing roadside sensing systems. 3. Install infrastructure-based sensing systems at selected sites based on deliverables from this project. 4. Implement and operate roadside sensing systems following the system engineering procedure. This proof-of-concept project will be based on existing roadside technologies, algorithms, and applications innovated by the project team in current and previous research. It is considered as a “Stage II Laboratory Prototype, Stage III Controlled Field Demonstration, and Stage IV Field Pilot Stage.” This research deployment will provide information necessary for NDOT and other traffic agencies to understand accuracy, reliability, and possible applications of roadside sensing systems in various real-world scenarios. The systems compared and developed in this project, the experience from proof-of-concept of various traffic scenarios, and the roadside sensor deployment guidance will directly guide the implementation of roadside animal detection systems. The RADS we envisage would be flexible in terms of deployability and could be deployed in areas that have these characteristics: 1) challenging to physically fence (e.g., presence of driveways and side roads), 2) sufficient line-of-sight for 100-200 m detection

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distances, 3) contain or lack infrastructure support (e.g., local AC power), 4) combined with or separated from fenced areas, and 5) with or without cell coverage. The implementation costs beyond the project include sensors and support infrastructure, driver warning signs/technology, and maintenance/replacement costs. Sensors and edge computing systems have reached a price-point that field-deployment in places like USA Parkway could cost on the order of $100,000 per ½ mile to 1 mile of covered roadway. This places the proposed system at a similar price point to physical fencing. 6. PROJECT SCHEDULE The proposed project is planned to be a 36-month project. The timelines are listed in Table 1.

Table 1 Project Schedule

PLAN PERIODS (3 YEARS) TASKS DURATION (Month) (Months) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 Task 1 - Project Management 36 Task 2 - Implementation 6 Task 3 - Deploy and Maint. 24 Site 1 Site 2 Task 4 - Data Analysis 6 Task 5 - System Communication 5 Task 6 - Deployment Guidance 3 Task 7 – Project report 3 7. FACILITIES AND EXPERTISE Dr. Hao Xu, Associate Professor in the Department of Civil and Environmental Engineering, Principal Investigator (PI) of the project, will oversee project progress management, manage human and research resources, and coordinate with NDOT. Dr. Xu’s research areas include roadside LiDAR data processing and, and data-driven traffic safety. Dr. Xu has been a leading multidisciplinary researcher on autonomous/connected vehicles, cybersecurity of transportation systems, smart cities, and LiDAR deployment for transportation applications (including detection of animals crossing roads). The collaboration between Dr. Xu’s research team, Velodyne (LiDAR manufacturer), RTC Washoe County, the City of Reno, RTC Southern Nevada, the City of Henderson, and NDOT has implemented the worldwide-first LiDAR-enhanced smart intersection in Reno and six additional intersections with permanent LiDAR installation in Nevada. Dr. Fraser Shilling co-director of the REC for a decade, will be a project co-PI. He has partnered with DOT staff in CA, ME, VT, VA, SD, ID, CO, AZ, NV, and GA to develop better ways to manage interactions between transportation and ecology, especially as it relates to wildlife-vehicle conflict. He has developed automated methods for GIS-determination of wildlife-vehicle conflict, the use of AI in wildlife detection and classification in camera imagery, and wildlife-responsive design of wildlife crossing structures. The Road Ecology Center (REC) is housed and supported by the Department of Environmental Science and Policy and the Institute of Transportation Studies (ITS), University of California, Davis. The REC owns camera/video equipment necessary for sensor data validation, including cell and Wi-Fi- communicating cameras; licensed GIS, statistical and image/video/sound analysis software computers. Importantly, the Center has developed web-accessible, AI-based image-processing

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tools to analyze images to determine if they are false-positives or animal-containing and to classify common animals (e.g., deer). Jeff Gagnon is a co-PI and has worked in the field of Road Ecology for more than 20 years and leads studies for Arizona Game and Fish Department (AZGFD) that focus on the planning, implementation, and evaluation of wildlife-vehicle conflict and habitat fragmentation mitigation in Arizona and multiple other states, including NV Boulder City Bypass Phase II Projects. Jeff also has been involved with the evaluation of animal detections systems for more than a decade including the AZ “Elk Crosswalk” thermal animal detection sFACILITY: AZGFD Wildlife Connectivity Group is housed in Phoenix, AZ with equipment and expertise needed for data review, evaluation, and validation of detection system data collected during the project. Tim Hazlehurst, the President of CrossTek EB Company, will provide and manage the implementation of Thermal Sensors and Radar Sensors within the scope of the animal detection and motorist warning project. Hazlehurst has worked in the field of wildlife control and safety for over 30 years in North America, Scandinavia, Europe, and Asia and has developed and installed thermal-sensor-based animal detection systems for warning motorists of wildlife crossing in Arizona and more recently in New Mexico. Hazlehurst brings a very capable team including Canadian based PBX engineering and the FLIR Thermal and Radar Company engineering groups, to support the deployment of Thermal and Radar-based sensors in the wildlife detection application. 8. BUDGET This proposal requests a total of $299,600 for the 3-year project, and budget details are in Table 2. The project team is open to negotiating with NDOT for a reduced budget related to an adjusted scope of work. 9. NDOT CHAMPION, COORDINATION AND INVOLVEMENT Nova Simpson, Northern Nevada Biological Supervisor, NDOT Environmental Division, phone (775) 888-7035, [email protected]. Eric Harmer, Signs, Striping, and Traffic Control Designer, NDOT Traffic Operations, phone (775) 888-7446, [email protected].

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Table 2 Estimated Project Budget

STANDARD BUDGET ITEMIZATION FOR DEPARTMENT RESEARCH PROJECTS Project Title: Comparison and Evaluation of Roadside Animal Sensing and Driver Warning Systems

Project Duration:3 years

Monthly % Total Position / % Fringe Name Total Fringe Benefit Salary or Wage Salary or Monthly Total Year 1 Title Benefit Hours Wage Associate Hao Xu Professor 2.30% $230 $10,660 94% $10,000 $10,230 Graduate TBD Student 12.10% $1,331 $1,850 595% $11,000 $12,331 Year 1 Total $1,561 $ $21,000 $22,561

Monthly % Total Position / % Fringe Name Total Fringe Benefit Salary or Wage Salary or Monthly Total Year 2 Title Benefit Hours Wage Assistant Hao Xu Professor 2.30% $115 $10,660 47% $5,000 $5,115 Graduate TBD Student 12.10% $1,331 $1,850 595% $11,000 $12,331 Year 2 Total $1,446 $ $16,000 $17,446

Monthly % Total Position / % Fringe Name Total Fringe Benefit Salary or Wage Salary or Monthly Total Year 3 Title Benefit Hours Wage Assistant Hao Xu Professor 2.30% $115 $10,660 47% $5,000 $5,115 Graduate TBD Student 12.10% $968 $1,850 432% $8,000 $8,968 Year 3 Total $1,083 $ $13,000 $14,083 Year 1 Year 2 Year 3 A. Personnel $22,561 $17,446 $14,083 B. Travel $300 $200 C. Operating Costs $2,500 $1,000 $0 D. Final Report Preparation and Submission $ $ $500 E. Other Costs $0 $ $

F. Subtotal of Direct Costs (sum of A thru E) $25,361 $18,646 $14,583

G. Total Indirect Cost (44% of modified indirect cost) $44,159 $8,204 $6,417

H. Student Tuition and Fees (included in the Indirect Cost calculation, UNR $1,410 $1,410 $1,410 requires) I.Suncontracts UC Davis Dr. Fraser Shilling - $78,000 for camera/video, analysis and interpretation of results; Tim Hazelhurst - $60,000 for Radar and thermal; $178,000 $0 $0 Jeff Gagnon, $40,000, for data validation and support Radar and thermal

K. TOTAL PROJECT COSTS PER YEAR (sum of F thru I) $248,930 $28,260 $22,410 TOTAL PROJECT COST $299,600

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REFERENCE

Gagnon, J. W., N. L. Dodd, S. C. Sprague, K. S. Ogren, C. D. Loberger, and R. E. Schweinsburg. 2019. Animal-activated highway crosswalk: long-term impact on elk-vehicle collisions, vehicle speeds, and motorist braking response. Human Dimensions of Wildlife:1-16.

Harmon et al., 2018, Crash Costs for Highway Safety Analysis. FHWA-SA-17-071.

Huijser, M. P., T. D. Holland, B. Matt, M. C. Greenwood, P. T. McGowen, B. Hubbard, and S. Wang. 2009. The comparison of animal detection systems in a test-bed: A quantitative comparison of system reliability and experience with operation and maintenance - final report. Prepared by Western Transportation Institute for Federal Highway Administration and Montana Department of Transportation.

Huijser, M.P., McGowan, P., Hardy, A., Kociolek, A., Clevenger, A.P., Smith, D. and Ament, R., 2017. Wildlife-vehicle collision reduction study: Report to congress.

NHTSA (2019) 2018 Fatal motor vehicle crashes: Overview. NHTSA Research Note DOT HS 812 826. Pp. 10. https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812826.

Redmon, J. and A. Farhadi (2018). “YOLOv3: An Incremental Improvement.” ArXiv abs/1804.02767.

Xu, H., Tian, Z., Wu, J., Liu, H. and Zhao, J., 2018. High-Resolution Micro Traffic Data grom Roadside LiDAR Sensors for Connected-Vehicles and New Traffic Applications. No. P224-14- 803 TO15.

Wu, J., Xu, H., Zhao, J.., 2018. Autonomous Wildlife Crossing Detection Method with Roadside Lidar Sensors. proceedings of the 97th Transportation Research Board Annual Meeting (No. 18-00500).

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12 Hao Xu, Ph.D., P.E. Department of Civil and Environmental Engineering Telephone: (775) 784-6909 University of Nevada, Reno Fax: (775) 784-1390 1664 N Virginia St, MS 258 Email: [email protected] Reno, NV 89557

Professional Preparation University of Science and Technology of China Hefei, China Automation BE, 2004 University of Science and Technology of China Hefei, China Automation ME, 2007 Texas Tech University Lubbock, TX Civil Engineering MS, 2009 Texas Tech University Lubbock, TX Civil Engineering Ph.D., 2011 Texas Tech University Lubbock, TX Transportation Engineering Postdoctoral Scholar, 2011-2013

Appointments Associate Professor, Department of Civil and Environmental Engineering, University of Nevada, Reno, 7/2019–Present. Assistant Professor, Department of Civil and Environmental Engineering, University of Nevada, Reno, 7/2013–6/2019. Products Most Closely Related Publications Sun, Y., H. Xu (corresponding author), J. Wu, J. Zheng and K. Dietrich, “3-D Data Processing to Extract Vehicle Trajectories from Roadside LiDAR Data”, Proceedings of the 97th Transportation Research Board Annual Meeting, January, Washington, D.C., 2018. Yang, G., H. Xu, Z. Wang and Z. Tian, “Truck Acceleration Behavior Study and Acceleration Lane Length Recommendations for Metered On-Ramps,” International Journal of Transportation Science and Technology, Vol.5(2), pp.93-102, 2016. Zhao, J., H. Xu (corresponding author), D. Wu and H. Liu, “An Artificial Neural Network to Identify Pedestrians and Vehicles from Roadside 360-Degree LiDAR Data”, Proceedings of the 97th Transportation Research Board Annual Meeting, January, Washington, D.C., 2018. Sun, Y., Xu, H., Wu, J., Hajj, E.Y. and Geng, X., 2017. Data processing framework for development of driving cycles with data from SHRP 2 Naturalistic Driving Study. Transportation Research Record, 2645(1), pp.50-56. Wu, J. and Xu, H., 2018. Driver behavior analysis on rural 2-lane, 2-way highways using SHRP 2 NDS data. Traffic injury prevention, 19(8), pp.838-843. Wu, J., Xu, H., Zheng, Y., Zhang, Y., Lv, B. and Tian, Z., 2019. Automatic Vehicle Classification using Roadside LiDAR Data. Transportation Research Record, p.0361198119843857. Volume: 2673 issue: 6, page(s): 153-164, 2019. Wu, J. and H. Xu, “The Influence of Road Familiarity on Distracted Driving Activities and Driving Operation using Naturalistic Driving Study Data,” Transportation research part F: traffic psychology and behavior 52: 75-85, 2018. Xu, H. and Wei, D., 2016. Improved Identification and Calculation of Horizontal Curves with Geographic Information System Road Layers. Transportation Research Record, 2595(1), pp.50- 58. Yang, G., Xu, H., Tian, Z. and Wang, Z., 2015. Vehicle speed and acceleration profile study for metered on-ramps in California. Journal of transportation engineering, 142(2), p.04015046. Yang, G., Xu, H., Tian, Z., Wang, Z. and Zhao, Y., 2015. Acceleration characteristics at metered on-ramps. Transportation Research Record, 2484(1), pp.1-9. Most Closely Related Projects PI, Pilot Deployment of Roadside LiDAR in City of Henderson NV, Regional Transportation Commission of Southern Nevada, $86,071, 2018 - 2020 PI, Proof-of-Concept Research of Roadside LiDAR Sensing Multimode Traffic, Nevada Department of Transportation, $313,397, 2018 - 2021 PI, Pilot Applications of Roadside LiDAR Technologies in Washoe County, Regional Transportation Commission – Washoe County, $250,000, 2018 - 2020 PI, High-Resolution Micro Traffic Data from Roadside LiDAR Sensors for Connected-Vehicles and New Traffic Applications, SOLARIS UTC and Nevada Department of Transportation, $173,670, 2017-2018 PI, Correlation Analysis of Nevada Crash Data and ITS Sensor Data, SOLARIS UTC and Nevada Department of Transportation, $52,447, 2016-2017 PI, Horizontal Curve Identification and Estimation, Nevada Department of Transportation, $44,281, 2015-2016 PI, Assessing the Influence of Driver, Vehicle, Roadway and Environment Factors on Pedestrian- Turning-Traffic Crashes at Intersections, SHRP 2, $100,000, 2015 PI, SHRP 2 Naturalistic Driving Study Data Usage Guidance for Nevada, SOLARIS UTC, $57,000, 2014-2015 PI, Evaluation of Restricted Truck Lanes in West Texas, Texas Department of Transportation, $53,251 , 2012 CO-PI, Queue Storage and Acceleration Lane Length Design for Metered On-ramps in California. California Department of Transportation, $249,310, 2013-2015. Senior Personnel, Enhanced Prediction of Vehicle Fuel Economy and Other Vehicle Operating Costs, FHWA, $1,240,000, 2/02/2014-09/18/2019 FRASER M. SHILLING

Department of Environmental Science and Policy Phone: (530) 752-7859 University of California, Davis FAX: (530) 752-3350 Davis, CA 95616 USA Email: [email protected]

EDUCATION 1991 Ph.D. (Aquatic Ecology), University of Southern California 1986 B.Sc. (Biology), University of Southern California

EMPLOYMENT 2016-present Faculty in Transportation Technology & Policy Graduate Group 2015-present Academic Coordinator II, Department of Environmental Science and Policy 2007-present Co-Director, UC Davis Road Ecology Center 2000-2014 Staff Research Associate IV, Department of Environmental Science and Policy 1998-2000 Research Coordinator, UC Center for Water and Wildlands Resources 1995-1998 Postdoctoral Fellow, Division of Biological Sciences University of California, Davis 1992-1994 Postdoctoral Fellow, University of Connecticut

RESEARCH INTERESTS Transportation ecology, the interactions of transportation systems with ecosystems and human communities; indicators of ecosystem performance, and policy issues associated with water pollution; impact of climate change/sea level rise on integrated planning of shoreline ecosystems and transportation.

SELECTED RECENT PEER-REVIEWED PUBLICATIONS Shilling, F., Collinson, W., Bíl, M., Vercayie, D., Heigl, F., Perkins, S., & MacDougall, S. (In Review) A review of wildlife-vehicle conflict observation systems. Biological Conservation. Schwartz, A, S. Perkins, F.M. Shilling (2020). The value of monitoring wildlife roadkill. European Journal of Wildlife Research. Volume 66, 18. https://doi.org/10.1007/s10344-019-1357-4 Tiedeman, K., R.J. Hijmans, A. Mandel, D.P. Waetjen, F. Shilling (2019) The quality and contribution of volunteer collected animal vehicle collision data in ecological research. Ecological Indicators. https://doi.org/10.1016/j.ecolind.2019.05.062 Seo, K., D. Salon, F. Shilling, M. Kuby (2018) Pavement condition and residential property values: a spatial hedonic price model for Solano County, CA. Public Works Management & Policy 23(3): 243-261. https://doi.org/10.1177/1087724X18757535 Ha, H. and F. Shilling (2018) Modelling potential wildlife-vehicle collisions (WVC) locations using environmental factors and human population density: A case-study from 3 state highways in Central California. Ecological Informatics 43 (2018) 212–221. https://doi.org/10.1016/j.ecoinf.2017.10.005. Waetjen, D.P. and F.M. Shilling (2017) Large extent roadkill and wildlife observation systems as sources of reliable data. Frontiers in Ecology and Evolution. http://journal.frontiersin.org/article/10.3389/fevo.2017.00089/full Shilling, F. and D.P. Waetjen (2017) Advancing environmental monitoring of highways with autonomous camera systems and web-informatics. Compendium of Papers for the Transportation Research Board Annual Conference, Washington DC, January 8-12 2017. Shilling, F.M., J. Vandever, K. May, I. Gerhard, and R. Bregoff. (2016) Adaptive planning for sea level rise-threatened transportation corridors. Transportation Research Record: Journal of the Transportation Research Board, No. 2599, Transportation Research Board, Washington, D.C., 2016, pp. 9–16. DOI: 10.3141/2599-02

SYNERGISTIC ACTIVITIES Journal Editor: Korean Journal of Civil Engineering (former Associate Editor); Journal Reviewer: 15 professional journals; Transportation Research Board: Strategic Highway Research Program 2: Expert Task Group (2007-2009); member TRB Ecology and Transportation Committee (ADC30, current); co-chair TRB Animal Vehicle Collision Subcommittee (ANB20-2, current); Federal Highways Administration: Eco-Logical Champion, providing on-call technical assistance to state DOTs and MPOs (2014-present) Lead Organizer, International Conference on Ecology and Transportation

TEACHING AND SUPERVISION Twenty-three graduate students from these graduate programs at UC Davis: Transportation Technology & Policy, Ecology, Geography, Animal Sciences, and Community Development. Courses: “General Ecology” (4-unit undergraduate class equivalent to UC Davis ESP100) at the Thai Nguyen University for Agriculture and Forestry, Vietnam; “Social Surveying Methods” (2 & 4-unit graduate course), for CRD and GGG methods credit, UC Davis; “Improving Community and Landscape Connectivity” (2-unit graduate seminar), Transportation Technology & Policy, UC Davis; "Road Ecology: Road Effect Zone" (2-unit graduate seminar), Transportation Technology & Policy, UC Davis. "Road Ecology" (4-unit graduate course), Transportation Technology & Policy, UC Davis.

SELECT REPORTS TO AGENCIES These reports were completed as part of funded projects for the named entities.

Shilling, F., Collins, A., Longcore, T, Vickers, W. 2020. Understanding Behavioral Responses of Wildlife to Traffic to Improve Mitigation Planning. Report of the National Center for Sustainable Transportation. https://escholarship.org/uc/item/72h3x0nk Shilling, F., Waetjen, D., and John, C. (2020). Automated Environmental Data Analysis and Management for State DOTs. Final report to FHWA under Agreement DTFH6117C00018. Pp. 23 Shilling F., C. Denney, and D. Waetjen. 2019. Automated Analysis of Wildlife-Vehicle Conflict Hotspots Using Carcass and Collision Data. A Research Report from the Pacific Southwest Region University Transportation Center. https://escholarship.org/uc/item/8h24v43z. Shilling, F. and D. Waetjen. (2019). California Water Indicators Portal (CWIP). Report to the US Environmental Protection Agency. Pp. 24. Wang, H. and F. Shilling. (2018) Land and Soil Remediation for Agriculture and Sustainability. Report to the Sha'anxi Engineering Group. Shilling, F.M., D. Waetjen, P. Cramer, and K. Harrold. 2017. Remote wireless wildlife camera systems, field testing, & informatics. Report to the Federal Highway Administration. 52 pages Shilling, F.M., J. Vandever, K. May, J. Villafranco, K.C. Ward, and D. Waetjen. 2016. State Route 37 integrated traffic, infrastructure and sea level rise analysis. Final Report to Caltrans. 116 pages. Shilling, F.M., et al. 2012. California pilot test of the ecological approaches to environmental protection developed in capacity research projects CO6A and CO6B. Report to the Transportation Research Board, Strategic Highway Research Program 2. 242 pages. Arizona Game and Fish JEFF W. GAGNON Department Road Ecologist/Statewide Research Biologist Wildlife Connectivity Group

BACKGROUND Mr. Gagnon has worked for the Arizona Game and Fish Department for 20 years with a primary focus on the field of Road Ecology and habitat connectivity, including wildlife research and project management. Jeff is considered a renowned expert in wildlife-highway relationships and has worked on numerous projects involving various highways and species throughout Arizona and other states, including elk, mule deer, white-tailed deer, pronghorn, Rocky Mountain and desert bighorn sheep. He has been the co-lead investigative biologist on the State Route 260 and US Highway 93 wildlife crossing projects. These are some of the most aggressive crossing projects in the nation, focused on reducing wildlife-vehicle collisions while maintaining habitat connectivity. Jeff’s specialty includes innovative solutions to WVC mitigation such as animal-activated detection systems, video surveillance, and GPS movements. Jeff provides wildlife-highway and habitat connectivity expertise at National and International wildlife-highway mitigation workshops and conferences and regularly publishes his findings in reports, peer-reviewed journals and conference proceedings. Due to the notoriety of wildlife-highway projects in Arizona that Jeff has played an important role, Phoenix, AZ was selected as the location of the 2013 International Conference on Ecology and Transportation. Outside of Arizona, Jeff assists multiple states and countries in their efforts to minimize wildlife-vehicle collisions while maintaining habitat connectivity. SELECT PROJECT EXPERIENCE

Boulder City Bypass Phase II Bighorn Sheep (2014-Current): Assist Nevada Departments of Wildlife and Transportation in design and implementation of measures to minimize collisions and habitat fragmentation of desert bighorn sheep. Primary investigator on a research and evaluation of sheep movements associated with construction of Boulder City Bypass Phase II and post-construction monitoring. State Route 260 Elk Crosswalk and Fencing Enhancement Project (2007-current): Oversee project to determine effectiveness of an animal-activated detection system, including GPS collared elk movements, wildlife-vehicle collision rates and wildlife and motorist behavior. Pilot study for use of fencing retrofits in various areas throughout Arizona and other states. These efforts are being replicated in other states with oversight by AZ Game and Fish Department State Route 77 Wildlife Crossing and Fencing Monitoring (2015-Current): Oversee effectiveness monitoring of wildlife- vehicle collision mitigation measures along SR 77 near Oracle, Arizona. Includes monitoring of the wildlife overpass and underpass via still and video camera surveillance, road kill data collection, and desert tortoise telemetry. Burro Interactions with Roadways, Phoenix AZ (2016-Current): Collaboration with ADOT to evaluate burro movements along select roadways in the Phoenix area to identify opportunities to minimize burro-vehicle collisions while simultaneously accounting for wildlife habitat connectivity. Includes GPS collaring of burros, camera monitoring of behaviors, burro-vehicle collision analysis and mobile app creation for tracking burro incidents along roadways. New Mexico Statewide Wildlife Crossing Monitoring (2017-current): Working with New Mexico DOT to evaluate statewide wildlife-vehicle collision mitigation efforts through camera trap and roadkill studies. Projects include cost-effective retrofits of existing drainage structures. I-17 Wildlife Fencing Retrofit Project (2012-2015) – Worked with ADOT on a cost-effective approach to retrofit existing drainage structures with fencing to serve as safe crossings for wildlife, post-construction monitoring showed a 97% reduction in elk-vehicle collisions and >100% increase in use of the drainage structures by wildlife. State Route 260 Wildlife-Highway Interaction Projects (2002-2008): Assist in projects to determine effectiveness of measures to reduce wildlife-vehicle collisions while maintaining habitat connectivity. Monitor and assess movements of collared animals. Design and implementing wildlife video surveillance systems for seven wildlife underpasses, ultimately collecting behavior data on >15,000 animals of 21 different species. US Highway 89 Pronghorn Movements North of I-40 (2006-current): Oversee capture, collaring and genetic sampling of 54 pronghorn to evaluate movements for identification of mitigation measure locations (underpasses/overpasses). Work with ADOT, USFS, NPS, Arizona Antelope Foundation and Babbitt Ranches on reconnecting pronghorn populations north of I-40 through aggressive fence and habitat modifications. US 93 Wildlife Overpasses and Fencing (2008-current): Oversee captures and collaring of >70 desert bighorn sheep to evaluate the effects of construction activities on sheep behavior and movements and effectiveness of wildlife crossings, including the first ever overpasses for desert bighorn sheep. Interstate 17, I-40, and State Route 260 Elk Movement Studies (2006-current): Oversee capture and collaring of >400 elk to evaluate effects of high traffic volume highways on elk movements. Work with ADOT and FHWA to identify wildlife crossing structure and fencing locations using GPS movement and wildlife-vehicle collision data.

SELECT RELEVANT PUBLICATIONS, REPORTS, PROCEEDINGS, AND BOOK CHAPTERS (2007-2020 Only)

Gagnon, J. W., N. L. Dodd, S. C. Sprague, K. S. Ogren, C. D. Loberger, and R. E. Schweinsburg. 2019. Animal-activated highway crosswalk: long-term impact on elk-vehicle collisions, vehicle speeds, and motorist braking response. Human Dimensions in Wildlife 1-16.

Gagnon, J. W., C. Loberger, K. Ogren , S. Sprague, S. Boe, and R. E. Schweinsburg. 2017. Evaluation of desert bighorn sheep overpass effectiveness: U.S. 93 Long-Term Monitoring. Final project report 710, ADOT Research Center, Phoenix, AZ.

Gagnon, J. W., C. Loberger, S. Sprague, S. Boe, K. Ogren, and R. E. Schweinsburg. 2017. Wildlife-vehicle collision mitigation on State Route 260: Mogollon Rim to Show Low. Final project report 706, ADOT Research Center, Phoenix, AZ.

Gagnon, J. W., C. Loberger, S. Sprague, S. Boe, K. Ogren, and R. E. Schweinsburg. 2016. Creating multi-use highway structures with retrofit fencing to reduce collisions with elk on Interstate-17. Final project report 689, ADOT Research Center, Phoenix, AZ.

Gagnon, J. W., C. D. Loberger, S. C. Sprague, K. S. Ogren, S. R. Boe, and R. E. Schweinsburg. 2015. Cost-effective approach to reducing collisions with elk by fencing between existing highway structures. Human-Wildlife Interactions 9(2):248-264.

Gagnon, J. W., N. L. Dodd, S. C. Sprague, C. D. Loberger, S. Boe, and R. E. Schweinsburg. 2014. Evaluation of measures to promote desert bighorn sheep highway permeability: U.S. Highway 93. Final project report 677. ADOT Research Center, Phx, AZ. Gagnon, J. W., N. L. Dodd, S. Sprague, R. Nelson, C. Loberger, S. Boe, and R. E. Schweinsburg. 2013. Elk movements associated with a high-traffic highway: Interstate 17. Final project report 647, ADOT Research Center, Phoenix, AZ.

Gagnon, J. W., C. D. Loberger, S. C. Sprague, M. Priest, K. Ogren, S. Boe, E. Kombe, and R. E. Schweinsburg. 2013. Evaluation of desert bighorn sheep overpasses along US Highway 93 in Arizona, USA. http://www.icoet.net/ICOET_2013/proceedings.asp

Gagnon, J. W., N. L. Dodd, K. S. Ogren, and R. E. Schweinsburg. 2011. Factors associated with use of wildlife underpasses and importance of long-term monitoring. Journal of Wildlife Management 75:1477-1487.

Gagnon, J. W., N. L. Dodd, S. C. Sprague, R. E. Nelson III, C. D. Loberger, S. Boe and R. E. Schweinsburg. 2011. Elk movements associated with interstate-17 in northern Arizona. Proceedings of the 2011 International Conference on Ecology and Transportation. Raleigh, NC: Center for Transportation and the Environment, North Carolina State University, Raleigh, North Carolina, USA. Gagnon, J. W., S. Sprague, S. Boe, R. Langley, H. S. Najar and R. E. Schweinsburg. 2011. Evaluation of Rocky Mountain bighorn sheep movements along US Highway 191 and Morenci Mine in Arizona, Pages 17-31 in 2011 Desert Bighorn Council Proc. #51. Gagnon, J. W., N. L. Dodd, S. Boe, and R. E. Schweinsburg. 2010. Using Global Positioning System technology to determine wildlife crossing structure placement and evaluating their success in Arizona, USA. Pages 452-462 in Proceedings of the 2009 International Conference on Ecology and Transportation. Center for Transportation and the Environment, North Carolina State University, Raleigh, USA., edited by P.J. Wagner, D. Nelson, and E. Murray. Raleigh, NC: Center for Transportation and the Environment, North Carolina State University.

Gagnon, J. W., N. L. Dodd, S. Sprague, K. Ogren, and R. E. Schweinsburg. 2010. Preacher Canyon wildlife fence and crosswalk enhancement project evaluation- State Route 260. Final project report Arizona Game and Fish Department, Phoenix, Arizona, USA.

Gagnon, J. W., T. C. Theimer, N. L. Dodd, A. L. Manzo, R. E. Schweinsburg. 2007. Effects of traffic on elk use of wildlife underpasses in Arizona. Journal of Wildlife Management 71(7): 2324-2328.

Gagnon, J. W., T. C. Theimer, N. L. Dodd, S. Boe, and R. E. Schweinsburg. 2007. Traffic volume alters elk distribution and highway crossings in Arizona. Journal of Wildlife Management 71(7): 2318-2323.

Gagnon, J. W., N. L. Dodd, R. E. Schweinsburg. 2007. Effects of roadway traffic on wild ungulates: A review of the literature and a case study of elk in arizona. Pages 449-458 in 2007 Proceedings of the International Conference on Ecology and Transportation. C. L. Irwin, D. Nelson, and K. P. McDermott, editors. Center for Transportation and the Environment, North Carolina State University, Raleigh, North Carolina, USA.

Book Chapters van der Ree, R., J. W. Gagnon, and D. J. Smith. 2016. Funnel Fencing - Handbook of Road Ecology. van der Ree, R., Smith, D.J. and Grilo, C (eds.). John Wiley & Sons, Oxford. 552 pp.

Dodd, N. L. and J. W. Gagnon. 2010. Arizona State Route 260: Promoting Wildlife Permeability, Highway Safety, and Agency Cultural Change. Pages 257-274 in Safe Passages: Highways, Wildlife, and Habitat Connectivity. Beckmann, J. P., A.P. Clevenger, M.P. Huijser, and J. A. Hilty, editors. Island Press, Washington D.C., USA.

Co-author

Keeley, T. H., P. Beier, and J. W. Gagnon. 2016. Estimating landscape resistance from habitat suitability: effects of data source and nonlinearities. Landscape Ecology: 1-12.

Dodd, N. L., J. W. Gagnon, S. Sprague, S. Boe, and R. E. Schweinsburg. 2012. Wildlife accident reduction study and monitoring: Arizona State Route 64. Final project report 626, Arizona Department of Transportation Research Center, Phoenix, AZ.

Dodd, N. L., J. W. Gagnon, S. Boe, K. Ogren, and R. E. Schweinsburg. 2012. Wildlife-vehicle collision mitigation for safer wildlife movement across highways: State Route 260. Final project report 603, Arizona Department of Transportation Research Center, Phoenix, AZ.

Dodd, N. L. and J. W. Gagnon. 2011. Influence of underpasses and traffic on white-tailed deer highway permeability. Wildlife Society Bulletin 35:270-281.

Dodd, N. L., J. W. Gagnon, S. Boe, and R. E. Schweinsburg. 2010. Evaluation of an animal-activated highway crosswalk integrated with retrofit fencing applications. Pages 603-612 in Proceedings of the 2009 International Conference on Ecology and Transportation. Center for Transportation and the Environment, North Carolina State University, Raleigh, USA., edited by P.J. Wagner, D. Nelson, and E. Murray. Raleigh, NC: Center for Transportation and the Environment, North Carolina State University, Raleigh, North Carolina, USA.

Dodd, N. L., J. W. Gagnon, S. Sprague, S. Boe, and R. E. Schweinsburg. 2010. Wildlife accident reduction study and monitoring: Arizona State Route 64. Final project report 626 Transportation Research Center, Arizona Department of Transportation, Phoenix, Arizona, USA.

Dodd, N. L., J. W. Gagnon, A. L. Manzo, R. E. Schweinsburg. 2007. Video surveillance to assess highway underpass use in Arizona. Journal of Wildlife Management 71(2): 637-645.

Dodd, N. L., J. W. Gagnon, S. Boe, and R. E. Schweinsburg. 2007. Assessment of elk highway permeability by using GPS telemetry. Journal of Wildlife Management 71(4): 1107-1117.

Dodd, N. L., J. W. Gagnon, S. Boe, A. Manzo, and R. E. Schweinsburg. 2007. Evaluation of measures to minimize wildlife-vehicle collisions and maintain permeability across highways: State Route 260, Arizona, USA (2002-2006). ADOT Final Report; SPR540; 169pp.

Dodd, N. L., J. W. Gagnon, S. Boe, and R. E. Schweinsburg. 2007. Role of fencing in promoting wildlife underpass use and highway permeability. Pages 475-487 in 2007 Proceedings of the International Conference on Ecology and Transportation. C. L. Irwin, D. Nelson, and K. P. McDermott, editors. Center for Transportation and the Environment, North Carolina State University, Raleigh, North Carolina, USA.

TIMOTHY HAZLEHURST

2212 Queen Anne Ave N Seattle, WA 98109 (330) 414-1995

Entrepreneur – Executive Manager

Entrepreneur, Founder, Company Builder and Manager with over 30 years experience. Total company development responsibility including; Team Leadership, Funding, Mergers and Acquisitions, Bottom line Performance, New Product Development, Multi- Channel Distribution, Marketing, Project and Sales management. Exceptional track record of successful strategic execution for creating growth in emerging and crowded markets. Deep experience in electronics design and manufacturing. Product application focus including Wildlife Vehicle Collision Mitigation solutions and production agriculture management tool development. Extensive track record of increasing sales and profit by leading optimization of team efforts and operational improvements resulting in quality improvement, increased productivity and reduced costs.

PROFESSIONAL EXPERIENCE

CrossTek EB, LLC CEO 2012 – Present Seattle, WA Managing and leading a start-up company with newly developed products for preventing Wildlife Vehicle Collisions. Specializing in Wildlife Barriers and Animal Detection and Motorist Warning Systems. Directing all aspects of company operations including planning, new product development, patents, sales, installations, finance, contract negotiations and results. Leading the market in a new category of products that repair ecosystem damage created by transportation corridors while increasing safety for motorists.

ElectroBraid Inc., Wildlife Division President 8/2009-12/2011 Payson, AZ General company management, strategic direction, project installation management for company turn-around and development of wildlife safety and habitat connectivity mitigation products. Specializing in Wildlife Barriers and Wildlife Fence. Provided leadership and strategic direction in all departments with full responsibility for turning around the bottom-line from annual $1 million losses to 11% net profit on revenue. Managed long-range planning, new product development, sales, and installations. Directed successful rollout of new wildlife mitigation products. TIMOTHY HAZLEHURST

AGRATRONIX President 12/2003-7/2009 Streetsboro, Ohio Executive leadership for dynamic $15 million international agricultural electronics company.12% annual growth in mature market with offices in USA and Finland and with EBIT over $1.75 million 3 years in a row. Lead operations and strategic direction with full responsibility for bottom-line. Manage long-range planning, global product management, product development process, key customer relationships, sales development and global manufacturing operations. Directed team of 8 managers in USA and Finland. Redefined organizational structure integrating product development and international marketing for domestic and international markets. Managed relocation of manufacturing operations from China to Thailand. Oversaw all financial decisions and company results.

FARMEX, Streetsboro, OH 12/2001-11/2003 Vice President – Electronics Division Successful acquisition and start up of new product division serving the North American market. Achieved operational profitability in less than one year and breakeven return on investment in less than 24 months Spearheaded vision, strategy and execution of business operations. Re-located manufacturing and sourcing functions from Mexico to Asia. Directed product engineering R&D team for completion of competitive product line-up. Directed national marketing and sales organization and key account relationships. Directed entire organization through prioritization and execution on most important company goals.

PREAGRO, Seattle, WA 8/1995 – 11/2001 President / Founder Started company specializing in manufacture of electric fence for wildlife exclusion, commercial agricultural animal management and hobby farmers. Developed product line and organically funded 5 years of growth leading to successful exit. Managed all aspects of company operations. Directed product design and development. Managed component sourcing, cost control and contract manufacturing in Mexico. Established domestic final assembly and test facility. Hired and managed marketing and sales team. Executed complete sale of company in last quarter of 2001.

EDUCATION:

BS Animal Science – University of Vermont