The document discusses improvements to a fuzzy-logic based map-matching method that incorporates trajectory stay-point detection to address data irregularities in vehicle GPS datasets. It evaluates the algorithm's accuracy and efficiency by analyzing the processing time and the number of correct links identified within several mapped trajectories. Key findings demonstrate that the proposed approach enhances the reliability and speed of map-matching compared to previous methods.

1. Improving Fuzzy-Logic based
Map-Matching Method with
Trajectory Stay-Point Detection
Authors: Minoo Jafarlou, Omid Mahdi Ebadati, and Hassan Naderi

2. Introduction
— Moving object databases have some irregularity, we need to preprocess the trajectory
— Vehicle GPS trajectory datasets include stay-points abnormality, which defines as noise in dataset
— Stay-points make matching algorithms mismatch trajectories to irrelevant roads

3. The map-matching process

4. Fuzzy-logic system blocks

5. Initial map-matching

6. Subsequent map-matching 1 (SMP1)
​θ↑′ is the vehicle's direction at previous point. Δd is d−d1. Abs
(θ−​θ↑′ ) is heading increment.

7. Subsequent map-matching 2 (SMP2)
d is the traveled distance by vehicle, d2= AO+OM, d3= AO+ON, d4= AO+OO’+OQ

8. Pseudo-code for fuzzy map-matching algorithm

9. Map-matching process

10. Different density levels

11. Stay-Point sample

12. Stay-Point
• Stay-point region misleads the map-matching process

13. Digital road network and tested GPS trajectory

14. Proposed method follow-chart

15. Three nearest neighbor distance curves

16. Number of clustered and non-clustered points for
different epsilons

17. Clustered stay-points data with DBSCAN

18. Accuracy performance evaluation
• Number of correct links detected among 399 links

19. Time efficiency evaluation
• Overall processing time comparison between two approaches

20. Volume efficiency evaluation
• The proposed approach decreased the data volume

21. Speed efficiency evaluation
• Average execution time speed for each point is calculated

22. Map-matching sample on the dataset

23. References
— [1] Jafarlou, Minoo, Omid Mahdi Ebadati E, and Hassan Naderi. "Improving Fuzzy-Logic based Map-
Matching Method with Trajectory Stay-Point Detection." arXiv e-prints (2022): arXiv-2208.
— [2] Schubert, Erich, et al. "DBSCAN revisited, revisited: why and how you should (still) use DBSCAN." ACM
Transactions on Database Systems (TODS) 42.3 (2017): 1-21.
— [3] Quddus, Mohammed A., Robert B. Noland, and Washington Y. Ochieng. "A high accuracy fuzzy logic
based map matching algorithm for road transport." Journal of Intelligent Transportation Systems 10.3 (2006):
103-115.