scholarly journals A Hidden Markov Model-Based Map Matching Algorithm for Low Sampling Rate Trajectory Data

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 178235-178245
Author(s):  
Yigong Hu ◽  
Binbin Lu
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Sampling Rate ◽  
Map Matching ◽  
Trajectory Data ◽  
Matching Algorithm ◽  
Model Based

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.

HMM-BiMM: Hidden Markov Model-based word segmentation via improved Bi-directional Maximal Matching algorithm

2021 ◽  
Vol 94 ◽  
pp. 107354
Author(s):  
Xingyu Yan ◽  
Xiaofan Xiong ◽  
Xiufeng Cheng ◽  
Yujing Huang ◽  
Haitao Zhu ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Word Segmentation ◽  
Matching Algorithm ◽  
Maximal Matching ◽  
Model Based

scholarly journals Map-Matching Using Hidden Markov Model and Path Choice Preferences under Sparse Trajectory

2021 ◽  
Vol 13 (22) ◽  
pp. 12820
Author(s):  
Zhengang Xiong ◽  
Bin Li ◽  
Dongmei Liu
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Road Network ◽  
Hidden Markov ◽  
Transition Probability ◽  
Shortest Paths ◽  
Low Frequency ◽  
Map Matching ◽  
Trajectory Data ◽  
Matching Accuracy

In the field of map matching, algorithms using topological relationships of road networks along with other data are normally suitable for high frequency trajectory data. However, for low frequency trajectory data, the above methods may cause problems of low matching accuracy. In addition, most past studies only use information from the road network and trajectory, without considering the traveler’s path choice preferences. In order to address the above-mentioned issue, we propose a new map matching method that combines the widely used Hidden Markov Model (HMM) with the path choice preference of decision makers. When calculating transition probability in the HMM, in addition to shortest paths and road network topology relationships, the choice preferences of travelers are also taken into account. The proposed algorithm is tested using sparse and noisy trajectory data with four different sampling intervals, while compared the results with the two underlying algorithms. The results show that our algorithm can improve the matching accuracy, especially for higher frequency locating trajectory. Importantly, the method takes into account the route choice preferences while correcting deviating trajectory points to the corresponding road segments, making the assumptions more reasonable. The case-study is in the city of Beijing, China.

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.

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