An Immune-Inspired Technique to Identify Heavy Goods Vehicles Incident Hot Spots

We report on the adaptation of an immune-inspired instance selection technique to solve a real-world big data problem of determining vehicle incident hot spots. The technique, which is inspired by the Immune System self-regulation mechanism, was originally conceptualised to eliminate very similar instances in data classification tasks. We adapt the method to detect hot spots from a telematics data set containing hundreds of thousands of data points indicating incident locations involving heavy goods vehicles (HGVs) across the United Kingdom. The objective is to provide HGV drivers with information regarding areas of high likelihood of incidents in order to continuously improve road safety and vehicle economy. The problem presents several challenges and constraints. An accurate view of the hot spots produced in a timely manner is necessary. In addition, the solution is required to be adaptable and dynamic, as thousands of new incidents are included in the database daily. Furthermore, the impact on hot spots after informing drivers about their existence has to be considered. Our solution successfully addresses these constraints. It is fast, robust, and applicable to all different incidents investigated. The method is also self-adjustable, which means that if the hot spots’ configuration changes with time, the algorithm automatically evolves to present the most current topology. Our solution has been implemented by a telematics company to improve HGV safety in the United Kingdom

Detecting danger in roads: an immune-inspired technique to identify heavy goods vehicles incident hot spots

We report on the adaptation of an immune-inspired instance selection technique to solve a real-world big data problem of determining vehicle incident hot spots. The technique, which is inspired by the Immune System self-regulation mechanism, was originally conceptualised to eliminate very similar instances in data classification tasks. We adapt the method to detect hot spots from a telematics data set containing hundreds of thousands of data points indicating incident locations involving heavy goods vehicles (HGVs) across the United Kingdom. The objective is to provide HGV drivers with information regarding areas of high likelihood of incidents in order to continuously improve road safety and vehicle economy. The problem presents several challenges and constraints. An accurate view of the hot spots produced in a timely manner is necessary. In addition, the solution is required to be adaptable and dynamic, as thousands of new incidents are included in the database daily. Furthermore, the impact on hot spots after informing drivers about their existence has to be considered. Our solution successfully addresses these constraints. It is fast, robust, and applicable to all different incidents investigated. The method is also self-adjustable, which means that if the hot spots’ configuration changes with time, the algorithm automatically evolves to present the most current topology. Our solution has been implemented by a telematics company to improve HGV safety in the United Kingdom

PAS3-HSID: a Dynamic Bio-Inspired Approach for Real-Time Hot Spot Identification in Data Streams

http://dx.doi.org/10.5902/2236130814684http://dx.doi.org/10.5902/2236130814684O gerenciamento de resíduos municipais é um tema que vem se tornando cada vez mais importante no contexto das preocupações mundiais dos governos, e teve um considerável desenvolvimento nas últimas décadas. Tanto os países desenvolvidos como os “em desenvolvimento” emitiram normativas legais restritivas, visando otimizar seus planos de tratamento e destinação final destes resíduos. O objetivo principal do trabalho é investigar a real situação deste cenário no Brasil e nos países desenvolvidos, demonstrando os resultados obtidos e traçando um paralelo comparativo e critico. São transcritos e analisados os dados obtidos, em cada fase de uma Gestão Integrada de Resíduos Sólidos Urbanos – GIRSU. Conclusões importantes são relatadas, tais como, o alto nível de investimento dos países desenvolvidos em relação às campanhas de conscientização para implantação de uma efetiva GIRSU, assim como contrastes marcantes entre os índices de reciclagem no Brasil e neste bloco diferenciado de países, ou seja, 2% e 20%, respectivamente, no montante dos resíduos totais gerados. A avaliação final é de que o diferencial esta nas ações políticas de incentivo econômico destes países desenvolvidos, em termos de subsídios, se comparados com o caso brasileiro

Detecting danger in roads: an immune-inspired technique to identify heavy goods vehicles incident hot spots

We report on the adaptation of an immune-inspired instance selection technique to solve a real-world big data problem of determining vehicle incident hot spots. The technique, which is inspired by the Immune System self-regulation mechanism, was originally conceptualised to eliminate very similar instances in data classification tasks. We adapt the method to detect hot spots from a telematics data set containing hundreds of thousands of data points indicating incident locations involving heavy goods vehicles (HGVs) across the United Kingdom. The objective is to provide HGV drivers with information regarding areas of high likelihood of incidents in order to continuously improve road safety and vehicle economy. The problem presents several challenges and constraints. An accurate view of the hot spots produced in a timely manner is necessary. In addition, the solution is required to be adaptable and dynamic, as thousands of new incidents are included in the database daily. Furthermore, the impact on hot spots after informing drivers about their existence has to be considered. Our solution successfully addresses these constraints. It is fast, robust, and applicable to all different incidents investigated. The method is also self-adjustable, which means that if the hot spots’ configuration changes with time, the algorithm automatically evolves to present the most current topology. Our solution has been implemented by a telematics company to improve HGV safety in the United Kingdom