Development of the next generation of smart salt trucks for sustainable winter road maintenance

开发下一代智能盐车以实现可持续的冬季道路维护

• 批准号: 537236-2018
• 负责人: Gharabaghi, Bahram
• 金额: $ 3.62万
• 依托单位: University of Guelph
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=698701
• 关键词: Development; next; generation; smart; salt

More than 5-million tonnes of road salt is used for de-icing roadways and parking lots in Canada each year. These salts can have a significant impact on water quality. Both environmental and health & safety concerns are becoming progressively important in the provision of road salt application plans. This research project will develop novel models for prescribing the right amount(s) of road salt and at the right time(s) to be applied judiciously on roadways and parking lots for a given storm event. This model will be using both remotely-sensed and ground-based, real-time weather forecast data to prescribe the optimum road salt application rates and timing of the applications within the storm event to achieve road safety while minimizing the harm to the salt-vulnerable areas. We will use the Road Weather Information System (RWIS) to more accurately forecast the severity and type of the winter storm events. We will then use advanced Artificial Intelligence modeling techniques, including extreme learning machine (ELM), gene expression programming (GEP), adaptive neuro-fuzzy inference systems (ANFIS), and the Generalized Method of Data Handling (GMDH) to train highly accurate machine learning models, using the detailed historic road salt application records for a range of complex winter storm events. This novel model can help more accurately prescribe the right amount(s) of road salt to be applied at the right time interval(s) on roadways and parking lots for a given winter storm event based on the real-time weather forecasts, available through the Road Weather Information System (RWIS), to protect the salt vulnerable areas. We will develop a LIDAR salt dispense measuring feedback loop system to allow the smart salt truck to self-calibrate and will create a graphical Human Machine Interface (HMI) for the operators to select routes and GPS-enabled salting profiles via a record and playback configuration or via a real-time control web portal design. The ultimate goal is to develop the next generation of the smart salt trucks equipped with intelligent systems that would allow for optimized salt application plan for higher road safety while better protecting identified salt vulnerable areas and at a lower cost.

加拿大每年有超过500万吨的道路盐用于道路和停车场的除冰。这些盐会对水质产生重大影响。环境和健康与安全问题在提供道路用盐应用计划方面变得越来越重要。该研究项目将开发新的模型，用于规定道路盐的正确数量，并在正确的时间明智地应用于给定风暴事件的道路和停车场。该模型将使用遥感和地面实时天气预报数据，规定最佳道路撒盐率和风暴事件内的应用时间，以实现道路安全，同时最大限度地减少对易受盐害地区的危害。我们将使用道路天气信息系统（RWIS）更准确地预测冬季风暴事件的严重程度和类型。然后，我们将使用先进的人工智能建模技术，包括极限学习机（ELM）、基因表达编程（GEP）、自适应神经模糊推理系统（ANFIS）和通用数据处理方法（GMDH）来训练高度准确的机器学习模型，使用一系列复杂冬季风暴事件的详细历史道路盐应用记录。这种新的模型可以帮助更准确地规定道路盐的正确数量（S）在正确的时间间隔（S）应用于给定的冬季风暴事件的道路和停车场基于实时天气预报，可通过道路天气信息系统（RWIS），以保护盐脆弱地区。我们将开发一个LIDAR盐分配测量反馈回路系统，以允许智能盐车进行自我校准，并将为操作员创建一个图形化的人机界面（HMI），以通过记录和回放配置或通过实时控制门户网站设计来选择路线和支持GPS的加盐配置文件。最终目标是开发配备智能系统的下一代智能运盐车，从而优化撒盐计划，提高道路安全性，同时更好地保护已确定的易受盐害地区，并降低成本。