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Estimating urban road gps environment friendliness with bus trajectories: A city-scale approach

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Estimating urban road gps environment friendliness with bus trajectories: A city-scale approach. / Ma, L.; Zhang, C.; Wang, Y. et al.
In: Sensors, Vol. 20, No. 6, 1580, 12.03.2020.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Ma, L, Zhang, C, Wang, Y, Peng, G, Chen, C, Zhao, J & Wang, J 2020, 'Estimating urban road gps environment friendliness with bus trajectories: A city-scale approach', Sensors, vol. 20, no. 6, 1580. https://doi.org/10.3390/s20061580

APA

Ma, L., Zhang, C., Wang, Y., Peng, G., Chen, C., Zhao, J., & Wang, J. (2020). Estimating urban road gps environment friendliness with bus trajectories: A city-scale approach. Sensors, 20(6), Article 1580. https://doi.org/10.3390/s20061580

Vancouver

Ma L, Zhang C, Wang Y, Peng G, Chen C, Zhao J et al. Estimating urban road gps environment friendliness with bus trajectories: A city-scale approach. Sensors. 2020 Mar 12;20(6):1580. doi: 10.3390/s20061580

Author

Ma, L. ; Zhang, C. ; Wang, Y. et al. / Estimating urban road gps environment friendliness with bus trajectories : A city-scale approach. In: Sensors. 2020 ; Vol. 20, No. 6.

Bibtex

@article{0d850126cedb4a059d7861a6e15c204e,
title = "Estimating urban road gps environment friendliness with bus trajectories: A city-scale approach",
abstract = "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.",
keywords = "GPS positioning error, Location-based service, Map matching, Matrix completion, Behavioral research, Encoding (symbols), Error analysis, Location based services, Roads and streets, Telecommunication services, Trajectories, User experience, Contextual information, Environment friendliness, Estimation strategies, GPS positioning, Method of processing, Positioning devices, Global positioning system",
author = "L. Ma and C. Zhang and Y. Wang and G. Peng and C. Chen and J. Zhao and J. Wang",
year = "2020",
month = mar,
day = "12",
doi = "10.3390/s20061580",
language = "English",
volume = "20",
journal = "Sensors",
issn = "1424-8220",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "6",

}

RIS

TY - JOUR

T1 - Estimating urban road gps environment friendliness with bus trajectories

T2 - A city-scale approach

AU - Ma, L.

AU - Zhang, C.

AU - Wang, Y.

AU - Peng, G.

AU - Chen, C.

AU - Zhao, J.

AU - Wang, J.

PY - 2020/3/12

Y1 - 2020/3/12

N2 - 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.

AB - 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.

KW - GPS positioning error

KW - Location-based service

KW - Map matching

KW - Matrix completion

KW - Behavioral research

KW - Encoding (symbols)

KW - Error analysis

KW - Location based services

KW - Roads and streets

KW - Telecommunication services

KW - Trajectories

KW - User experience

KW - Contextual information

KW - Environment friendliness

KW - Estimation strategies

KW - GPS positioning

KW - Method of processing

KW - Positioning devices

KW - Global positioning system

U2 - 10.3390/s20061580

DO - 10.3390/s20061580

M3 - Journal article

VL - 20

JO - Sensors

JF - Sensors

SN - 1424-8220

IS - 6

M1 - 1580

ER -