在這科技日新月異的社會，人們的時間變得相當珍貴，因此當他們在開車或搭車前能得知準確的行車時間便是一項重要的議題，然而準確的行車時間預測建立在一定數量且有利用價值的歷史資料，但是毫無策略一味的蒐集資料相當沒有效率，並且會帶來許多不好的後果，像是伺服器端會有嚴重的負擔、本地端的設備可能會因蒐集太多不必要的資料而造成無謂的電量消耗。
我們將會針對行車時間預測的歷史資料蒐集進行策略性的取樣，並且希望儘管使用較少的資料，一樣能達成未使用任何策略性採樣的結果。我們著重於空間及時間這兩個在現實中最容易影響行車時間的關鍵因素，另外再加入資料密集程度的概念，透過減少蒐集密集度較高區域的資料，希望能忽略掉一些不必要或是多餘的資料。最後除了各個方法的結果比較，我們也會透過視覺化的方式來觀察搜集到的資料以便更了解整體資料的分布情況。

In the modern society, people often need to know how long they will spend on driving. To predict the travel time accurately is an important issue. It based on the enough and useful historical data. But, if we collect the historical data without any strategy, it will bring some bad consequences. The burden of the server will be too heavy, and for local side, it will result in some unnecessary power consumption when collecting the data.
In this thesis, we focus on the problem of sampling the data in trip time prediction. We want to achieve the same result by using less data. Space and time are the two main factors we consider, and we also propose the concept of data density. By reducing the data in high density areas, we can ignore some redundant data. At last we will show the results of our method and compare with each other. In addition, some visualization of the data sampled will be presented so that we can clearly understand the data distribution.

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