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One of the keys in time-dependent routing is determining the weight for each road network link based on symmetrical and complete traffic information. To facilitate travel planning considering traffic situations based on historical global position system (GPS) trajectory data which uncover the whole road network, this paper proposes a fuzzy random forest-based road section data estimation method, which uses the third law of geography as the core idea. For different time periods, road grade, tidal lane, proximity to infrastructure (main places that affect traffic, such as schools, hospitals), and accident road sections were selected as indicators that influence the traffic. The random forest algorithm is used to build the mapping relationship between attribute data with average traffic which is obtained based on GPS data. Subsequently, the fuzzy reasoning method is used to obtain the weight of road links missing traffic information by calculating their similarities with typical road section samples. Using the road network of Suzhou City as an example, the proposed method was used to analyze estimate the average driving speeds of road sections with missing traffic information for different time periods. Experimental results show that this method can effectively avoid congested road sections and obtain high-speed travel routes. [ABSTRACT FROM AUTHOR]