處在這充滿通訊、資訊設備的時代，所有的交通資訊都可以迅速的傳送給駕駛者，同時駕駛者也可以將他們的感知回傳給系統。對駕駛者來說，找一條「最短路徑」已經不足以滿足他們的需求，還必須要考慮其他的路徑特徵、駕駛者的感知、和環境的狀況。
此篇研究的目的就是要建立一個路徑導引系統，根據駕駛者的喜好去選擇一條「最合適的路徑」。透過模糊類神經的演算，使系統能夠根據駕駛者的經驗去學習駕駛者的決策邏輯，讓系統更加適性化。此研究利用模糊推論系統去推論駕駛者的決策行為，透過Dijkstra's演算法找到從起點到終點的最合適路徑，最後用ANFIS去學習駕駛者的行為。

In this age full with communication and information technologies, all information includes route distance, time, and congestion degree can be transformed to drivers easily. Users can also return their perceptions back to the system. So it's not enough that only consider the shortest distance when drivers make route choice, but also other route characteristics, their own perceptions and situational factors.
The objective of this work is to model a useful route guidance system which has the capability to support the driver deciding on an optimum route based on his preference. Besides, a neuro-fuzzy approach is used to learn the decision logic from drivers' experience and make the system adaptive. Therefore, an integrated adaptive driving route guidance system is built to deal with multiple uncertain and vague decision variables from driver's decision behavior through fuzzy TSK inference system. It provides one path based on driver's route choice behavior between origin and destination arbitrarily without alternatives through Dijkstra's algorithm. It also learns driver's route choice logic by itself without adjusting the system by driver himself through adaptive-network-based fuzzy inference system (ANFIS).

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