Quadtree-based domain decomposition for parallel map-matching on GPS data

Xia, Yingjie; Liu, Yuncai; Ye, Zhoumin; Wu, Wei; Zhu, Mingzhe

doi:10.1109/itsc.2012.6338600

Cited by 20 publications

(11 citation statements)

References 18 publications

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“…With the topology relationships among the road links in a network, and the probability between estimated positions and nearby road links, HMM-based map-matching can be implemented to find the actual trajectory [11,[42][43][44].…”

Section: Hmm-based Map-matchingmentioning

confidence: 99%

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

Luo

Chen²,

Xv³

2017

IJGI

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Numerous map-matching techniques have been developed to improve positioning, using Global Positioning System (GPS) data and other sensors. However, most existing map-matching algorithms process GPS data with high sampling rates, to achieve a higher correct rate and strong universality. This paper introduces a novel map-matching algorithm based on a hidden Markov model (HMM) for GPS positioning and mobile phone positioning with a low sampling rate. The HMM is a statistical model well known for providing solutions to temporal recognition applications such as text and speech recognition. In this work, the hidden Markov chain model was built to establish a map-matching process, using the geometric data, the topologies matrix of road links in road network and refined quad-tree data structure. HMM-based map-matching exploits the Viterbi algorithm to find the optimized road link sequence. The sequence consists of hidden states in the HMM model. The HMM-based map-matching algorithm is validated on a vehicle trajectory using GPS and mobile phone data. The results show a significant improvement in mobile phone positioning and high and low sampling of GPS data.

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Section: Hmm-based Map-matchingmentioning

confidence: 99%

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

Luo

Chen²,

Xv³

2017

IJGI

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“…We take the geospatial computation on GPS data as the example. As shown in Figure 2, the GPS data on a region where there are numerous road segments in Shanghai are divided into data pieces on sub-domains based on the region quadtree and balancing size of data pieces strategy [23]. The data are collected from 8172 taxis belonging to Jinjiang and Bashi taxi companies on 2077 road segments.…”

Section: Region-based Its Data Divisionmentioning

confidence: 99%

A Generic Methodological Framework for Cyber-ITS: Using Cyber-infrastructure in ITS Data Analysis Cases

Xia

Zhang

Wang

2014

Fundamenta Informaticae

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This paper presents a method to capture the computational intensity and computing resource requirements of data analysis in intelligent transportation systems (ITS). These requirements can be transformed into a generic methodological framework for Cyber-ITS, mainly consisting of region-based ITS data divisions and tasks scheduling for processing, to support the efficient use of cyber-infrastructure (CI). To characterize the computational intensity of a particular ITS data analysis, the computational transformation is performed by data-centric and operation-centric transformation functions. The application of this framework is illustrated by two ITS data analysis cases: multi-sensor data fusion for traffic state estimation by integrating rough set theory with Dempster-Shafer (D-S) evidence theory, and geospatial computation on Global Positioning System (GPS) data for advertising value evaluation. To make the design of generic parallel computing solutions feasible for ITS data analysis in these cases, an approach is developed to decouple the region-based division from specific high performance computer (HPC) architecture and implement a prototype of the methodological framework. Experimental results show that the prototype implementation of the framework can be applied to divide the ITS data analysis into a load-balanced set of computing tasks, therefore facilitating the parallelized data fusion and geospatial computation to achieve remarkable speedup in computation time and throughput, without loss in accuracy.

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“…The whole data processing including the fusion is computation intensive and requires to be parallelized to implement the real-time traffic state estimation. 25 Therefore, in this paper we propose a fusion model capable for processing heterogeneous transportation data and implement its parallelized version to improve the computing efficiency.…”

Section: Related Workmentioning

confidence: 99%

Parallelized Fusion on Multisensor Transportation Data: A Case Study in CyberITS

Xia

Shan

2013

Int. J. Intell. Syst.

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With increasing requirements on traffic efficiency, environmental quality, energy efficiency, and economic productivity, intelligent transportation systems (ITS) research, which is centered on traffic state evaluation, meets two grand challenges. One is to process heterogeneous transportation data collected from different types of traffic sensors and the other is to reduce the high computation intensity for processing massive transportation data. To overcome both challenges, this paper proposes an approach using parallelized fusion on multisensor transportation data. Parallelized fusion is an embodied case of CyberITS framework, which is developed for the synthesis of cyberinfrastructure and ITS. The fusion functionality is shaped by a rough evidential fusion model (REFM) based on rough set and Dempster-Shafer evidence theories. The REFM consists of four components, which are sensor data input, bootstrapping rough conversion, hierarchical evidential fusion, and traffic state output. Their computation intensity is centered on conversion and fusion components, which can be optimized by the algorithm-and data-centric parallelization, respectively. Computational experiments for accuracy and efficiency demonstrate that parallelized fusion achieves a distributed, high-performance, and collaborative CyberITS implementation to provide accurate and real-time traffic state evaluation. C 2013 Wiley Periodicals, Inc.

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Quadtree-based domain decomposition for parallel map-matching on GPS data

Cited by 20 publications

References 18 publications

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

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

A Generic Methodological Framework for Cyber-ITS: Using Cyber-infrastructure in ITS Data Analysis Cases

Parallelized Fusion on Multisensor Transportation Data: A Case Study in CyberITS

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