利用多變量變異數分析與半母數線性模型辨識行進中車輛

Abstract

道路上的即時資料是交通管理系統中重要的一環，為了測得在眾多道路上車輛所在的車道與種類，我們利用架設在路旁的無線電射頻系統晶片來收集車輛的資訊。當車子進入偵測區時，晶片所記錄的資料是雷達回波的強度。而原始的資料經過適當的截取及平移後，可視為一筆函數資料。本文中提出兩種模型方法來辨識車種，其中一種是利用多變量分析方法來說明車種與車道之間主變因與其交互作用的影響，另一種則是用半母數線性模型來突顯函數資料中各群的特徵。若分的群數不多，不論用哪種模型分析，都可以得到不錯的結果；但當群數過多時，則會降低此兩種模型分析的能力。

In order to make the detecting of the lanes and the types of the vehicles traveling on various roadways affordable, radio-frequency (RF) system-on-chip is designed and will be mounted on the roadside to collect vehicle information. The data originally collected by the chip is the intensity of the back wave of the vehicle entering the range of detection. The raw data is registered by landmark and then treated as functional data. In order to classify the types of the vehicles, two models are proposed to model the data. One is multivariate analysis of variance model to account for the main effect and the interaction effect between type and lane, the other is the semi-parametric linear model to emphasize the functional characteristic of the data. Both models work well when the number of groups is small but deteriorate when the number of groups increases.