利用無母數分類方法辨識動態車輛

Abstract

塞車是日常生活中常見的問題，塞車主因之一是車道不足或車道無法 有效利用，所以辨識常塞車路段的車種，統計各車種流量，將有助於 車道、專用車道及交通號誌的建置與控管，進而有效解決塞車問題。 由於雷達微波辨識系統建置成本較影像辨識系統低廉，本文所考慮是 一組雷達微波資料集，資料包含反射波強度及車種的紀錄，資料經由 適當的截取及平移，此筆資料可視為函數資料，因此利用函數資料的 無母數分群方法來辨識車種，並比較三種不同函數資料的近似度測度，找出辨識率最高的方法。

Traffic congestion is a serious and general problem in our daily life. Real time traveling vehicle information is essential to the advanced traffic management. The recognition and statistics of traffic flow among different types of traveling vehicles would be contributive to improve traffic block. This thesis considers the dataset recorded by the Radar microwave detector with the intensity of back waves and the types of traveling vehicles. The data is treated as functional data and then classification would be proposed to be performed by nonparametric discrimination of functional data with three forms of Proximity. The proximity with lower misclassification rate would be adopted for the data. The results show that the misclassification rate is pretty satisfactory if the number of groups is two and as the number of groups increases the misclassification rate increases as expected.