Abstract
As a fundamental component of trajectory processing and analysis, trajectory map-matching can be used for urban traffic management and tourism route planning, among other applications. While there are many trajectory map-matching methods, urban high-sampling-frequency GPS trajectory data still depend on simple geometric matching methods, which can lead to mismatches when there are multiple trajectory points near one intersection. Therefore, this study proposed a novel segmented trajectory matching method in which trajectory points were separated into intersection and non-intersection trajectory points. Matching rules and processing methods dedicated to intersection trajectory points were developed, while a classic “Look-Ahead” matching method was applied to non-intersection trajectory points, thereby implementing map matching of the whole trajectory. Then, a comparative analysis between the proposed method and two other new related methods was conducted on trajectories with multiple sampling frequencies. The results indicate that the proposed method is not only competent for intersection matching with high-frequency trajectory data but also superior to two other methods in both matching efficiency and accuracy.
Highlights
Due to the popularity of mobile positioning devices, a significant volume of trajectory data with various types is generated
The results show that this method and the decision domain hidden Markov model (HMM) method are more suitable for high-frequency data, while the long common subsequence (LCS) method is more suitable for low-frequency data
This study has proposed a segmented matching method by which trajectory matching is divided into intersection matching and non-intersection matching
Summary
Due to the popularity of mobile positioning devices, a significant volume of trajectory data with various types is generated. Trajectory data acquisition depends on different positioning devices that vary in terms of accuracy errors, where the trajectories deviate from the original road or points of interest. Map matching is required before processing and analyzing trajectory data [1]. Trajectory map-matching is required to add semantic information to trajectory data and attach geographic ground information to trajectories. In the past few decades, many map matching methods have been proposed. These methods can be divided into geometric, topological, and advanced methods [1], or they can be divided into local and global methods [2].
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