The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
도로 일반화는 지도의 크기에 따라 쉽게 볼 수 있도록 도로망을 얇게 만드는 방법입니다. 대부분의 기존 도로 일반화 방법은 주로 도로, 하천 등 네트워크 데이터에 적용되는 지각적 그룹화 원리를 기반으로 연속성이 좋은 링크 체인인 스트로크 길이에 중점을 둡니다. 그러나 웹맵 서비스(예: 레스토랑 안내도)에서의 시설 검색의 경우, 스트로크 길이뿐만 아니라 시설 검색 결과에도 의존한다면 도로 일반화 메커니즘이 더 효과적일 수 있습니다. 이에 본 연구에서는 시설 검색 결과와 스트로크 길이 모두에 적응하는 주문형 도로 일반화 방법을 구현한다. 또한, 웹맵 서비스에 실용화하기에는 충분히 빠른 응답속도를 달성한다. 특히, 본 연구에서는 시설 정보를 개별 스트로크와 연계한 지방 스트로크 모델을 제안하고, 이 모델을 활용하여 응답 시간을 향상시키는 도로 일반화 방법을 구현합니다. 또한 제안된 시스템을 기반으로 프로토타입을 개발한다. 시스템 평가 결과는 도로 일반화 시스템의 응답시간, 스트로크 간 연결성, 스트로크와 시설 간 연결성 등 100가지 지표를 기반으로 한다. 우리의 실험 결과는 제안된 방법이 더 높은 연결성을 제공하면서 XNUMX배 이상의 향상된 응답 시간을 제공할 수 있음을 시사합니다.
Daisuke YAMAMOTO
Nagoya Institute of Technology
Masaki MURASE
Nagoya Institute of Technology
Naohisa TAKAHASHI
Nagoya Institute of Technology
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부
Daisuke YAMAMOTO, Masaki MURASE, Naohisa TAKAHASHI, "On-Demand Generalization of Road Networks Based on Facility Search Results" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 1, pp. 93-103, January 2019, doi: 10.1587/transinf.2017EDP7405.
Abstract: Road generalization is a method for thinning out road networks to allow easy viewing according to the size of the map. Most conventional road generalization methods mainly focus on the length of a stroke, which is a chain of links with good continuity based on the principle of perceptual grouping applied to network data such as roads and rivers. However, in the case of facility search in a web map service, for example, a “restaurant guide map,” a road generalization mechanism can be more effective if it depends not only on the stroke length but also on the facility search results. Accordingly, in this study, we implement an on-demand road generalization method that adapts to both the facility search results and the stroke length. Moreover, a sufficiently fast response speed is achieved for practical use in web map services. In particular, this study proposes a fat-stroke model that links facility information to individual strokes and implements a road generalization method that uses this model to improve the response time. In addition, we develop a prototype based on the proposed system. The system evaluation results are based on three indicators, namely, response time of the road generalization system, connectivity between strokes, and connectivity between stroke and facilities. Our experimental results suggest that the proposed method can yield improved response times by a factor of 100 or more while affording higher connectivity.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017EDP7405/_p
부
@ARTICLE{e102-d_1_93,
author={Daisuke YAMAMOTO, Masaki MURASE, Naohisa TAKAHASHI, },
journal={IEICE TRANSACTIONS on Information},
title={On-Demand Generalization of Road Networks Based on Facility Search Results},
year={2019},
volume={E102-D},
number={1},
pages={93-103},
abstract={Road generalization is a method for thinning out road networks to allow easy viewing according to the size of the map. Most conventional road generalization methods mainly focus on the length of a stroke, which is a chain of links with good continuity based on the principle of perceptual grouping applied to network data such as roads and rivers. However, in the case of facility search in a web map service, for example, a “restaurant guide map,” a road generalization mechanism can be more effective if it depends not only on the stroke length but also on the facility search results. Accordingly, in this study, we implement an on-demand road generalization method that adapts to both the facility search results and the stroke length. Moreover, a sufficiently fast response speed is achieved for practical use in web map services. In particular, this study proposes a fat-stroke model that links facility information to individual strokes and implements a road generalization method that uses this model to improve the response time. In addition, we develop a prototype based on the proposed system. The system evaluation results are based on three indicators, namely, response time of the road generalization system, connectivity between strokes, and connectivity between stroke and facilities. Our experimental results suggest that the proposed method can yield improved response times by a factor of 100 or more while affording higher connectivity.},
keywords={},
doi={10.1587/transinf.2017EDP7405},
ISSN={1745-1361},
month={January},}
부
TY - JOUR
TI - On-Demand Generalization of Road Networks Based on Facility Search Results
T2 - IEICE TRANSACTIONS on Information
SP - 93
EP - 103
AU - Daisuke YAMAMOTO
AU - Masaki MURASE
AU - Naohisa TAKAHASHI
PY - 2019
DO - 10.1587/transinf.2017EDP7405
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2019
AB - Road generalization is a method for thinning out road networks to allow easy viewing according to the size of the map. Most conventional road generalization methods mainly focus on the length of a stroke, which is a chain of links with good continuity based on the principle of perceptual grouping applied to network data such as roads and rivers. However, in the case of facility search in a web map service, for example, a “restaurant guide map,” a road generalization mechanism can be more effective if it depends not only on the stroke length but also on the facility search results. Accordingly, in this study, we implement an on-demand road generalization method that adapts to both the facility search results and the stroke length. Moreover, a sufficiently fast response speed is achieved for practical use in web map services. In particular, this study proposes a fat-stroke model that links facility information to individual strokes and implements a road generalization method that uses this model to improve the response time. In addition, we develop a prototype based on the proposed system. The system evaluation results are based on three indicators, namely, response time of the road generalization system, connectivity between strokes, and connectivity between stroke and facilities. Our experimental results suggest that the proposed method can yield improved response times by a factor of 100 or more while affording higher connectivity.
ER -