Map-matching approach based on link factor and hidden Markov model

2021 ◽  
pp. 1-17
Author(s):  
Haiyan Zhang ◽  
Yonglong Luo ◽  
Qingying Yu ◽  
Xiaoyao Zheng ◽  
Xuejing Li
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Road Network ◽  
Hidden Markov ◽  
Transition Probability ◽  
Map Matching ◽  
The Road ◽  
Positioning Errors ◽  
Road Section ◽  
Gps Devices

An accurate map matching is an essential but difficult step in mapping raw float car trajectories onto a digital road network. This task is challenging because of the unavoidable positioning errors of GPS devices and the complexity of the road network structure. Aiming to address these problems, in this study, we focus on three improvements over the existing hidden Markov model: (i) The direction feature between the current and historical points is used for calculating the observation probability; (ii) With regard to the reachable cost between the current road section and the destination, we overcome the shortcoming of feature rarefaction when calculating the transition probability with low sampling rates; (iii) The directional similarity shows a good performance in complex intersection environments. The experimental results verify that the proposed algorithm can reduce the error rate in intersection matching and is suitable for GPS devices with low sampling rates.

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.

Analysis of Consumption of Rural Residents in Jilin Province Based on Hidden Markov Model

2014 ◽  
Vol 971-973 ◽  
pp. 2281-2284
Author(s):  
Xin Zhao ◽  
Qian Sun ◽  
Yan Hong Huang Fu ◽  
Chao Ran Li
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Transition Probability ◽  
Jilin Province ◽  
Probability Estimation ◽  
Rural Residents ◽  
Future Situation ◽  
Estimation Function ◽  
Sample Interval

Analysis status consumption of residents according to the statistical data in the recently twenty years of rural residents in Jilin province the Engel Coefficient.Select the sample interval properly based on hidden markov model,modeled using MATLAB and estimate the transition probability between states using probability estimation function of MATLAB’s hidden markov model toolbox, contact probability estimation in Markov model toolbox function, and predicting the Engel Coefficients of rural residents in the province for the next ten years (2013-2022). Studies have shown that, using the hidden Markov model established by MATLAB can accurately predict the future situation of residents consumption.

scholarly journals Classification of Online Game Players Using Action Transition Probability and Kullback Leibler Entropy

2007 ◽  
Vol 11 (3) ◽  
pp. 319-326 ◽  
Author(s):  
Ruck Thawonmas ◽  
◽  
Ji-Young Ho ◽  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Transition Probability ◽  
Online Game ◽  
Classification Approach ◽  
Game Players ◽  
Better Than

Online game players are more satisfied with contents tailored to their preferences. Player classification is necessary for determining which classes players belong to. In this paper, we propose a new player classification approach using action transition probability and Kullback Leibler entropy. In experiments with two online game simulators, Zereal and Simac, our approach performed better than an existing approach based on action frequency and comparably to another existing approach using the Hidden Markov Model (HMM). Our approach takes into account both the frequency and order of player action. While HMM performance depends on its structure and initial parameters, our approach requires no parameter settings.

Recovering Truncated Streaming Event Log Using Coupled Hidden Markov Model

2019 ◽  
Vol 34 (04) ◽  
pp. 2059012
Author(s):  
Riyanarto Sarno ◽  
Kelly Rossa Sungkono
Keyword(s):  
Information Systems ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Process Model ◽  
Hidden Markov ◽  
Transition Probability ◽  
Process Discovery ◽  
Model Based ◽  
Event Logs ◽  
Event Log

Process discovery is a technique for obtaining process model based on traces recorded in the event log. Nowadays, information systems produce streaming event logs to record their huge processes. The truncated streaming event log is a big issue in process discovery because it inflicts incomplete traces that make process discovery depict wrong processes in a process model. Earlier research suggested several methods for recovering the truncated streaming event log and none of them utilized Coupled Hidden Markov Model. This research proposes a method that combines Coupled Hidden Markov Model with Double States and the Modification of Viterbi–Backward method for recovering the truncated streaming event log. The first layer of states contains the transition probability of activities. The second layer of states uses patterns for detecting traces which have a low appearance in the event log. The experiment results showed that the proposed method recovered appropriately the truncated streaming event log. These results also have proven that the accuracies of recovered traces obtained by the proposed method are higher than those obtained by the Hidden Markov Model and the Coupled Hidden Markov Model.

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