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A map matching algorithm based on modified hidden Markov model considering time series dependency over larger time

Ha Yoon Song¹, Jae Ho Lee¹

¹Department of Computer Engineering, Hongik University, 94 Wausan-ro, Mapo-gu, Seoul, South Korea.

|November 13, 2023

Summary

This summary is machine-generated.

A new trendHMM map matching algorithm improves trajectory data accuracy by considering neighboring data points. This enhanced method outperforms traditional Hidden Markov Models (HMM) in complex environments.

Keywords:

Geopositioning data Hidden Markov model Map matching Trajectory data

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Area of Science:

Geomatics Engineering
Data Science
Mobile Computing

Background:

Map matching is crucial for refining inaccurate trajectory data from geopositioning systems.
Hidden Markov Models (HMM) are popular for map matching but oversimplify time-series dependencies.
Complex urban environments and data errors challenge existing HMM-based map matching algorithms.

Purpose of the Study:

To introduce trendHMM, an enhanced map matching algorithm addressing HMM limitations.
To improve the accuracy of trajectory data matching in diverse conditions.
To leverage relationships among continuous geopositioning data for better inference.

Main Methods:

Proposed the trendHMM algorithm, incorporating adjacent data points using a sliding window.
Enhanced the Hidden Markov Model (HMM) by considering broader dependencies in geopositioning data.
Conducted experiments to evaluate trendHMM against traditional HMM map matching.

Main Results:

trendHMM demonstrated significant performance enhancements over the standard HMM algorithm.
The algorithm achieved up to 17.58% improvement in Route Mismatch Fraction.
Experimental results confirmed trendHMM's superior accuracy across various environments and datasets.

Conclusions:

trendHMM map matching offers a more robust solution for trajectory data preprocessing.
The algorithm effectively handles complex road networks and movement patterns.
Incorporating neighboring data dependencies enhances the reliability of map matching.