現代人生活忙碌，時時刻刻都必須分秒必爭，因此時常希望在短時間之內獲得大量的訊息，包括哪裡有停車場、停車場還有沒有空位、哪裡交通比較順暢或更多多媒體訊息，若這類與位置相關的訊息，能使用預取的策略，事先將資料載入使用者的無線行動裝置中，則使用者將可以節省大量的時間，反之，使用者可能會不斷的在搜尋已經不再需要的資料，因此使用資料預取策略來解決等待時間及無線網路不穩定的問題，已經是眾人所探討的問題之一。
人的移動行為是一個不確定的系統，常常會受到生活週遭不同因素的影響，包括地形、時間等，若是想要預測人類的移動行為，則必須從這些影響因素著手，因此，本論文使用灰色理論中的灰關聯分析，作為預測人類移動行為的方法，期望藉由考慮多種影響人類行為的因素，使得預取系統更適當的下載使用者所需要的物件，並根據使用者的預取命中率，透過二段調變臨界值，對預取模型進行二段式調變，進而提升命中率以及減少物件預取量。
最後於模擬測試中，比較距離式預取、位置分割式預取、路徑式資料預取及二段式灰關聯資料預取等策略的效能。並以命中率及物件預取量作為比較指標，由模擬結果可得知，二段式灰關聯資料預取的效能優於其他三種方法。

People who live in these times seize every minute and second. They always catch a lot of information including: where have parking area, whether there is a vacancy at the parking area, where smooth traffic and more multimedia message at short time. If user uses data prefetching policy that loads information about location to mobile host beforehand, he will save a large of time. On the contrary, he will search useless information constantly. So people use data prefetching policy to solve the problem that waiting time and wireless network line is unstable, it is one of the questions that everybody discusses.
People's movement is an uncertain system that often be influenced by the factor of surroundings, including landform, time etc.. If we want to predict the human movement, we will need to consider the influence factors. So, this research uses the grey relation analysis to predict People's movement, and expects that mobile host can prefetch proper object by considering the influencing factors. Especially, this research set two-layer adaptive law so that it promote hit ratio and reduce prefetching object.
Finally, we compare two-layer data prefetching policy with other policies, including distance-base data prefetching policy, partition-data prefetching policy and path-base data prefetching policy. The above comparisons are based on the two evaluation factors: hit ratio and prefetching object. The experimental results verify that the two-layer data prefetching policy is better than others.

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