Fast Hidden Markov Model Map-Matching for Sparse and Noisy Trajectories

2015 ◽  
Author(s):  
Hannes Koller ◽  
Peter Widhalm ◽  
Melitta Dragaschnig ◽  
Anita Graser
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Map Matching

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.

Hidden Markov model and driver path preference for floating car trajectory map matching

2018 ◽  
Vol 12 (10) ◽  
pp. 1433-1441 ◽  
Author(s):  
Chengbo Song ◽  
Xuefeng Yan ◽  
Nkyi Stephen ◽  
Arif Ali Khan
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Map Matching

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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