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Self-adaptive online map matching method based on hidden Markov model

A map matching and self-adapting technology, applied to road network navigators and other directions, can solve the problems of reduced matching accuracy, increased output delay, and loss of effective information, and achieve the effect of reducing energy consumption

Inactive Publication Date: 2018-10-12
TIANJIN UNIV
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Problems solved by technology

However, for online scenarios, reducing the number of samples will cause a lot of effective information to be lost, resulting in a significant drop in matching accuracy
The simple solution is to increase the sliding window size, but this in turn leads to a significant increase in output latency

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  • Self-adaptive online map matching method based on hidden Markov model
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  • Self-adaptive online map matching method based on hidden Markov model

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Embodiment Construction

[0041] GPS trajectory data plays a vital role in various trajectory service-based applications. However, due to many factors such as measurement error, sampling error and battery power saving requirements, these directly obtained trajectory data cannot be accurately matched to the digital map. Based on this, we need to propose an online map matching method that has the characteristics of high precision, low latency and energy saving at the same time. In order to achieve the above requirements, the present invention needs to realize the following three technologies: 1) In order to ensure the accuracy, the present invention will combine the geometric information of the spatial road network, the topology information and the basic idea of ​​probability matching, and then use these comprehensive information to complete the matching task; 2) The present invention proposes an adaptive sampling frequency method, which can enable the vehicle to adaptively determine the sampling frequen...

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Abstract

The invention relates to the field of mobile intelligent transportation and provides a self-adaptive online map matching method based on a hidden Markov model. The method provides a self-adaptive online map matching framework based on a hidden Markov model, provides accurate, low-latency and low-energy consumption online map matching services for various track-based application programs and realizes visualization through API provided by the map service provider. The method comprises acquiring trajectory information, inputting the trajectory sequences as input parameters of a matching system, orderly carrying out candidate analysis treatment, probabilistic analysis processing and self-adaptive matching processing on the trajectory sequences to obtain the real-time matching results of the trajectory sequences and feeding back the matching results as output to the desired application program. The self-adaptive online map matching method is mainly used in the mobile intelligent traffic occasions.

Description

technical field [0001] The invention relates to the field of mobile intelligent transportation, in particular to an adaptive online map matching method based on a hidden Markov model. Background technique [0002] In recent years, with the development of GPS (Global Positioning System, short for Global Positioning System) positioning equipment, a large amount of GPS trajectory data can be obtained in real time from GPS smart devices such as taxis and smart phones. Using these trajectory data, online route planning, traffic incident detection, and travel time prediction can be easily realized, that is, online matching services can be provided to those location-based applications. [0003] However, when collecting GPS trajectory data, it is often accompanied by measurement errors, sampling errors, and energy consumption limitations, so that these trajectory data cannot be used directly. Due to the limitations of GPS technology itself, including the measurement, transmission a...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01C21/32
CPCG01C21/32
Inventor 冯志勇安琪陈世展黄科满何东晓
Owner TIANJIN UNIV
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