Cargando…

Estimating Urban Road GPS Environment Friendliness with Bus Trajectories: A City-Scale Approach †

GPS is taken as the most prevalent positioning system in practice. However, in urban areas, as the GPS satellite signal could be blocked by buildings, the GPS positioning is not accurate due to multi-path errors. Estimating the negative impact of urban environments on GPS accuracy, that is the GPS e...

Descripción completa

Detalles Bibliográficos
Autores principales: Ma, Liantao, Zhang, Chaohe, Wang, Yasha, Peng, Guangju, Chen, Chao, Zhao, Junfeng, Wang, Jiangtao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7146484/
https://www.ncbi.nlm.nih.gov/pubmed/32178298
http://dx.doi.org/10.3390/s20061580
_version_ 1783520213570945024
author Ma, Liantao
Zhang, Chaohe
Wang, Yasha
Peng, Guangju
Chen, Chao
Zhao, Junfeng
Wang, Jiangtao
author_facet Ma, Liantao
Zhang, Chaohe
Wang, Yasha
Peng, Guangju
Chen, Chao
Zhao, Junfeng
Wang, Jiangtao
author_sort Ma, Liantao
collection PubMed
description GPS is taken as the most prevalent positioning system in practice. However, in urban areas, as the GPS satellite signal could be blocked by buildings, the GPS positioning is not accurate due to multi-path errors. Estimating the negative impact of urban environments on GPS accuracy, that is the GPS environment friendliness (GEF) in this paper, will help to predict the GPS errors in different road segments. It enhances user experiences of location-based services and helps to determine where to deploy auxiliary assistant positioning devices. In this paper, we propose a method of processing and analysing massive historical bus GPS trajectory data to estimate the urban road GEF integrated with the contextual information of roads. First, our approach takes full advantage of the particular feature that bus routes are fixed to improve the performance of map matching. In order to estimate the GEF of all roads fairly and reasonably, the method estimates the GPS positioning error of each bus on the roads that are not covered by its route, by taking POIinformation, tag information of roads, and building layout information into account. Finally, we utilize a weighted estimation strategy to calculate the GEF of each road based on the GPS positioning performance of all buses. Based on one month of GPS trajectory data of 4835 buses within the second ring road in Chengdu, China, we estimate the GEF of 8831 different road segments and verify the rationality of the results by satellite maps, street views, and field tests.
format Online
Article
Text
id pubmed-7146484
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-71464842020-04-20 Estimating Urban Road GPS Environment Friendliness with Bus Trajectories: A City-Scale Approach † Ma, Liantao Zhang, Chaohe Wang, Yasha Peng, Guangju Chen, Chao Zhao, Junfeng Wang, Jiangtao Sensors (Basel) Article GPS is taken as the most prevalent positioning system in practice. However, in urban areas, as the GPS satellite signal could be blocked by buildings, the GPS positioning is not accurate due to multi-path errors. Estimating the negative impact of urban environments on GPS accuracy, that is the GPS environment friendliness (GEF) in this paper, will help to predict the GPS errors in different road segments. It enhances user experiences of location-based services and helps to determine where to deploy auxiliary assistant positioning devices. In this paper, we propose a method of processing and analysing massive historical bus GPS trajectory data to estimate the urban road GEF integrated with the contextual information of roads. First, our approach takes full advantage of the particular feature that bus routes are fixed to improve the performance of map matching. In order to estimate the GEF of all roads fairly and reasonably, the method estimates the GPS positioning error of each bus on the roads that are not covered by its route, by taking POIinformation, tag information of roads, and building layout information into account. Finally, we utilize a weighted estimation strategy to calculate the GEF of each road based on the GPS positioning performance of all buses. Based on one month of GPS trajectory data of 4835 buses within the second ring road in Chengdu, China, we estimate the GEF of 8831 different road segments and verify the rationality of the results by satellite maps, street views, and field tests. MDPI 2020-03-12 /pmc/articles/PMC7146484/ /pubmed/32178298 http://dx.doi.org/10.3390/s20061580 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ma, Liantao
Zhang, Chaohe
Wang, Yasha
Peng, Guangju
Chen, Chao
Zhao, Junfeng
Wang, Jiangtao
Estimating Urban Road GPS Environment Friendliness with Bus Trajectories: A City-Scale Approach †
title Estimating Urban Road GPS Environment Friendliness with Bus Trajectories: A City-Scale Approach †
title_full Estimating Urban Road GPS Environment Friendliness with Bus Trajectories: A City-Scale Approach †
title_fullStr Estimating Urban Road GPS Environment Friendliness with Bus Trajectories: A City-Scale Approach †
title_full_unstemmed Estimating Urban Road GPS Environment Friendliness with Bus Trajectories: A City-Scale Approach †
title_short Estimating Urban Road GPS Environment Friendliness with Bus Trajectories: A City-Scale Approach †
title_sort estimating urban road gps environment friendliness with bus trajectories: a city-scale approach †
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7146484/
https://www.ncbi.nlm.nih.gov/pubmed/32178298
http://dx.doi.org/10.3390/s20061580
work_keys_str_mv AT maliantao estimatingurbanroadgpsenvironmentfriendlinesswithbustrajectoriesacityscaleapproach
AT zhangchaohe estimatingurbanroadgpsenvironmentfriendlinesswithbustrajectoriesacityscaleapproach
AT wangyasha estimatingurbanroadgpsenvironmentfriendlinesswithbustrajectoriesacityscaleapproach
AT pengguangju estimatingurbanroadgpsenvironmentfriendlinesswithbustrajectoriesacityscaleapproach
AT chenchao estimatingurbanroadgpsenvironmentfriendlinesswithbustrajectoriesacityscaleapproach
AT zhaojunfeng estimatingurbanroadgpsenvironmentfriendlinesswithbustrajectoriesacityscaleapproach
AT wangjiangtao estimatingurbanroadgpsenvironmentfriendlinesswithbustrajectoriesacityscaleapproach