Our aim of this paper is to develop new map matching algorithms and to
improve upon previous work. We address two key approaches: Analytic Hierarchy
Process (AHP) map matching and fuzzy logic map matching. AHP is a
decision-making method that combines mathematical analysis with human judgment,
and fuzzy logic is an approach to computing based on the degree of truth and
aims at modeling the imprecise modes of reasoning from 0 to 1 rather than the
usual boolean logic. Of these algorithms, the way of our applying AHP to map
matching is newly developed in this paper, meanwhile, our application of fuzzy
logic to map matching is mostly the same as existing research except for some
small changes. Because of the common characteristic that both methods are
designed to handle imprecise information and simplicity for implementation, we
decided to use these methods.

本文的目标是开发新的地图匹配算法并改进之前的工作，涵盖了分析层次过程法（AHP）地图匹配和模糊逻辑地图匹配，这些算法中使用 AHP 进行地图匹配的方法是本文新开发的，而使用模糊逻辑进行地图匹配的应用与现有研究基本相同，只有一些小的改变。由于这两种方法都适用于处理不精确信息并且易于实现，我们决定使用这些方法。

基于分析层次过程和模糊逻辑的两种在线地图匹配算法

Two Online Map Matching Algorithms Based on Analytic Hierarchy Process  and Fuzzy Logic

Rough membership function defines the measurement of relationship between
conditional and decision attribute from an Information system. In this paper we
propose a new method to construct rough graph through rough membership function
$\omega_{G}^F(f)$. Rough graph identifies the pattern between the objects with
imprecise and uncertain information. We explore the operations and properties
of rough graph in various stages of its structure.

本文提出了通过粗糙集近似度函数构建粗糙图的新方法，以便识别具有不精确和不确定信息的对象之间的模式，同时探讨了粗糙图在其结构的各个阶段中的操作和属性。