scholarly journals Map-matching algorithm based on the junction decision domain and the hidden Markov model

PLoS ONE ◽  
2019 ◽  
Vol 14 (5) ◽  
pp. e0216476 ◽  
Author(s):  
Hui Qi ◽  
Xiaoqiang Di ◽  
Jinqing Li
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Map Matching ◽  
Matching Algorithm

A Hidden Markov Model-Based Map-Matching Algorithm for Wheelchair Navigation

2009 ◽  
Vol 62 (3) ◽  
pp. 383-395 ◽  
Author(s):  
Ming Ren ◽  
Hassan A. Karimi
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Viterbi Algorithm ◽  
Hidden Markov ◽  
Markov Chain Model ◽  
Computational Time ◽  
Chain Model ◽  
Map Matching ◽  
Matching Algorithm ◽  
Matching Techniques

Application of map-matching techniques to GPS positions can provide accurate vehicle location information in challenging situations. The Hidden Markov Model (HMM) is a statistical model that is well known for providing solutions to temporal recognition applications such as text and speech recognition. This paper introduces a novel map-matching algorithm based on HMM for GPS-based wheelchair navigation. Given GPS positions, a hidden Markov chain model is established by using both geometric data and the topology of sidewalk segments. The map-matching algorithm employs the Viterbi algorithm to estimate correct sidewalk segments as hidden states in a HMM in order to match GPS trajectory on the corresponding segment sequence. The HMM-based map-matching algorithm was validated on a campus sidewalk network for wheelchair navigation. The results show an improvement in tracking a wheelchair in dense urban conditions both in accuracy and in computational time.

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

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xiao Fu ◽  
Jiaxu Zhang ◽  
Yue Zhang
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Second Order ◽  
Map Matching ◽  
Trajectory Data ◽  
Matching Problem ◽  
Matching Algorithm ◽  
Study Results

Map matching is a key preprocess of trajectory data which recently have become a major data source for various transport applications and location-based services. In this paper, an online map matching algorithm based on the second-order hidden Markov model (HMM) is proposed for processing trajectory data in complex urban road networks such as parallel road segments and various road intersections. Several factors such as driver’s travel preference, network topology, road level, and vehicle heading are well considered. An extended Viterbi algorithm and a self-adaptive sliding window mechanism are adopted to solve the map matching problem efficiently. To demonstrate the effectiveness of the proposed algorithm, a case study is carried out using a massive taxi trajectory dataset in Nanjing, China. Case study results show that the accuracy of the proposed algorithm outperforms the baseline algorithm built on the first-order HMM in various testing experiments.

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