There is an increasing interest worldwide in animal detection systems to reduce animal-vehicle collisions. Traditional approaches include building animal crossings, introducing real or virtual fencing, video surveillance and break-the-beam systems. Unlike these approaches, the system described here Large Animal Warning and Detection System (LAWDS) employs a 360˚-scanning radar to monitor a stretch of highway. This provides year-round continuous highway monitoring, even in harsh weather conditions. Innovative analysis and classification techniques enable the system to track large animals (e.g. deer). Low false alarm rate and environmental impact make LAWDS attractive for operational use. LAWDS also distinguishes vehicles from large animals and analyzes highway traffic metrics such as traffic volume and vehicle speeds.
Abstract. Ambient air pollution continues to be a major human health burden around the world. Cities with existing smart data infrastructure, and those with smart city aspirations, would benefit from the integration of real-time data from an air quality sensor network. AirSENCE™ is one such sensor which monitors eight common pollutants at low cost. It has been deployed in Canada in cities of the Greater Toronto Area, e.g. the City of Oshawa, to augment the existing urban data network and study the impacts of traffic flow and land usage on air quality. Results reveal that distributed sensors are highly useful for detecting localized pollution events that would otherwise go undetected, providing policymakers with a valuable, actionable data for protecting public health. Coupling air quality sensors with other smart city data (traffic monitors in this case) was shown to provide a more comprehensive representation of how air pollutant levels are affected by human activity, which can better inform city planning decisions.
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