An Online Map Matching Algorithm Based on Second-Order Hidden Markov Model

Fu, Xiao; Zhang, Jiaxu; Zhang, Yue

doi:10.1155/2021/9993860

Cited by 8 publications

(3 citation statements)

References 40 publications

(48 reference statements)

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“…In Eq (6), the line is represented as Ax 0 + By 0 + C = 0, where A, B, and C are the parameters of the line. The first K segments closest to the test point p are selected as candidate segments [36,37], and the projection points from the processing point p to each candidate segment are PLOS ONE calculated based on the selected candidate segments e 1 , e 2 . .…”

Section: Design Of Map Matching Algorithm Based On the Hmmmentioning

confidence: 99%

Traffic trajectory data analysis technology based on HMM model map matching algorithm

Sun,

2024

PLoS ONE

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The rapid growth of traffic trajectory data and the development of positioning technology have driven the demand for its analysis. However, in the current application scenarios, there are some problems such as the deviation between positioning data and real roads and low accuracy of existing trajectory data traffic prediction models. Therefore, a map matching algorithm based on hidden Markov models is proposed in this study. The algorithm starts from the global route, selects K nearest candidate paths, and identifies candidate points through the candidate paths. It uses changes in speed, angle, and other information to generate a state transition matrix that match trajectory points to the actual route. When processing trajectory data in the experiment, K = 5 is selected as the optimal value, the algorithm takes 51 ms and the accuracy is 95.3%. The algorithm performed well in a variety of road conditions, especially in parallel and mixed road sections, with an accuracy rate of more than 96%. Although the time loss of this algorithm is slightly increased compared with the traditional algorithm, its accuracy is stable. Under different road conditions, the accuracy of the algorithm is 98.3%, 97.5%, 94.8% and 96%, respectively. The accuracy of the algorithm based on traditional hidden Markov models is 95.9%, 95.7%, 95.4% and 94.6%, respectively. It can be seen that the accuracy of the algorithm designed has higher precision. The experiment proves that the map matching algorithms based on hidden Markov models is superior to other algorithms in terms of matching accuracy. This study makes the processing of traffic trajectory data more accurate.

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Section: Design Of Map Matching Algorithm Based On the Hmmmentioning

confidence: 99%

Traffic trajectory data analysis technology based on HMM model map matching algorithm

Sun,

2024

PLoS ONE

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“…The Markov property implies that the distribution of the current state depends on the distribution of the previous state, making the distribution of the initial states influential, especially for those states within a certain amount time in the early stage of navigation, despite the stationary distribution property. Traditionally, the uniform distribution is commonly used to specify the initial state distribution and the emission probability distribution is typically based solely on the distance between GPS measurements and road segments [13,26,27,39]. However, taking just the distance into account may be insufficient due to the large GPS error.…”

Section: Problem Statementsmentioning

confidence: 99%

An Enhanced Hidden Markov Model for Map-Matching in Pedestrian Navigation

Ma,

Wang,

Lee

2024

Electronics

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Map-matching is a core functionality of pedestrian navigation applications. The localization errors of the global positioning systems (GPSs) in smartphones are one of the most critical factors that limit the large-scale deployment of pedestrian navigation applications, especially in dense urban areas where multiple road segments exist within the range of GPS errors, which can be increased by tall buildings neighboring each other. In this paper, we address two issues of practical importance for map-matching based on the Hidden Markov Model (HMM) in pedestrian navigation systems: large localization error in the initial phase of map-matching and HMM breaks in open field traversals. A heuristic method to determine the probability of initial states of the HMM based on a small number of GPS data received during the short warm-up period is proposed to improve the accuracy of initial map-matching. A simple but highly practical method based on a heuristic evaluation of near-future locations is proposed to prevent the malfunction of the Viterbi algorithm within the area of open fields. The results of field experiments indicate that the enhanced HMM constructed via the proposed methods achieves significantly higher map-matching accuracy compared to that of state of the art.

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“…The HMM has been widely used for map-matching applications and optimized for different settings and environments (see, e.g., [12,24,28,40,46]. An essential Fig.…”

Section: Brief Overview Of Available Approachesmentioning

confidence: 99%

A Practical Guide to an Open-Source Map-Matching Approach for Big GPS Data

Saki

Hagen

2022

SN COMPUT. SCI.

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This work shows how map-matching helps to minimize errors in GPS data by finding the most probable corresponding points of the recorded waypoints of a trajectory on a road network. We investigate an open-source alternative for map-matching trajectories called Valhalla, which could replace limited and costly commercial map-matching services. Valhalla is an open-source routing engine, which provides different services, such as path-finding, map-matching, and generating maneuvers based on a path. We build a cloud-based big data analytics framework on Amazon Web Services (AWS) platform for map-matching. This well-established framework is scalable and could process millions of trajectories. Using an example GPS dataset, it is demonstrated how Valhalla can be used for map-matching at scale. The dataset consists of about 18 million trips in the year 2019 that have at least one recorded point in a bounding box surrounding Frankfurt am Main. The map-matching results confirm an adequate performance of Valhalla map-matching, show a reduction of errors by distance calculation, and allow for further street-segment-based analysis.

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An Online Map Matching Algorithm Based on Second-Order Hidden Markov Model

Cited by 8 publications

References 40 publications

Traffic trajectory data analysis technology based on HMM model map matching algorithm

Traffic trajectory data analysis technology based on HMM model map matching algorithm

An Enhanced Hidden Markov Model for Map-Matching in Pedestrian Navigation

A Practical Guide to an Open-Source Map-Matching Approach for Big GPS Data

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