Smart refueling can reduce costs and lower the possibility of an emergency. Refueling intelligence can only be obtained by mining historical refueling behaviors from big data, however, without devices, such as fuel ta...Smart refueling can reduce costs and lower the possibility of an emergency. Refueling intelligence can only be obtained by mining historical refueling behaviors from big data, however, without devices, such as fuel tank cursors, and cooperation from drivers, these behaviors are hard to detect. Thus, detecting refueling behaviors from big dala derived from easy-to-approach trajectories is one of/he most efficient retrieve evidences for research of refueling behaviors. In this paper, we describe a complete procecdure for detecting refoeling behavior in big data derived from freight trajectories. This procedure involves the inte- gration of spatial data mining and machine-learning techniques. The key pall of the methodology is a pattern detector that extends the naive Bayes classifier. By draw＇ing on the spatial and temporal characteristics of freight trajectories, refileling behaviors can be identified with high accuracy. Fu,lher, we present a refueling prediction and recommendation system to show how our refueling detector can be used practically in big data. Our experimetlts on real trajeclories show that our refueling detector is accurate, and the system performs well.展开更多
基金supported by a grant from the Science Technology and Innovation Committee of Shenzhen Municipality
文摘Smart refueling can reduce costs and lower the possibility of an emergency. Refueling intelligence can only be obtained by mining historical refueling behaviors from big data, however, without devices, such as fuel tank cursors, and cooperation from drivers, these behaviors are hard to detect. Thus, detecting refueling behaviors from big dala derived from easy-to-approach trajectories is one of/he most efficient retrieve evidences for research of refueling behaviors. In this paper, we describe a complete procecdure for detecting refoeling behavior in big data derived from freight trajectories. This procedure involves the inte- gration of spatial data mining and machine-learning techniques. The key pall of the methodology is a pattern detector that extends the naive Bayes classifier. By draw＇ing on the spatial and temporal characteristics of freight trajectories, refileling behaviors can be identified with high accuracy. Fu,lher, we present a refueling prediction and recommendation system to show how our refueling detector can be used practically in big data. Our experimetlts on real trajeclories show that our refueling detector is accurate, and the system performs well.
