Enhanced Map-Matching Algorithm with a Hidden Markov Model for Mobile Phone Positioning

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

Journal of Advanced Transportation ◽

10.1155/2021/9993860 ◽

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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A Stochastic Map Matching Method for Floating Car Data Based on a Hidden Markov Model

CICTP 2015 ◽

10.1061/9780784479292.028 ◽

2015 ◽

Author(s):

Ningyu Zhao ◽

Zhiheng Li

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Hidden Markov ◽

Map Matching ◽

Matching Method ◽

Floating Car Data ◽

Stochastic Map

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Detection of Mobile Phone Fraud Using Possibilistic Fuzzy C-Means Clustering and Hidden Markov Model

International Journal of Synthetic Emotions ◽

10.4018/ijse.2016070102 ◽

2016 ◽

Vol 7 (2) ◽

pp. 23-44 ◽

Cited By ~ 1

Author(s):

Sharmila Subudhi ◽

Suvasini Panigrahi ◽

Tanmay Kumar Behera

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Mobile Phone ◽

Hidden Markov ◽

Clustering Methods ◽

Fuzzy C Means ◽

Novel Approach ◽

Incoming Call ◽

Fuzzy C Means Clustering ◽

Reality Mining

This paper presents a novel approach for fraud detection in mobile phone networks by using a combination of Possibilistic Fuzzy C-Means clustering and Hidden Markov Model (HMM). The clustering technique is first applied on two calling features extracted from the past call records of a subscriber generating a behavioral profile for the user. The HMM parameters are computed from the profile, which are used to generate some profile sequences for training. The trained HMM model is then applied for detecting fraudulent activities on incoming call sequences. A calling instance is detected as forged when the new sequence is not accepted by the trained model with sufficiently high probability. The efficacy of the proposed system is demonstrated by extensive experiments carried out with Reality Mining dataset. Furthermore, the comparative analysis performed with other clustering methods and another approach recently proposed in the literature justifies the effectiveness of the proposed algorithm.

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