Clarifying Origin-Destination Flows Using Force-Directed Edge Bundling Layout
Origin-destination (OD) flows have time-varying characteristics and spatial heterogeneity. With the increasing amount of urban travel data, it is very challenging to clarify OD flows through traditional visualization methods. A novel approach based on the force-directed edge bundling (FDEB) algorith...
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doaj-03d2f12be8324818851a8e583d53b7bf2021-03-30T03:07:42ZengIEEEIEEE Access2169-35362020-01-018625726258310.1109/ACCESS.2020.29830529046019Clarifying Origin-Destination Flows Using Force-Directed Edge Bundling LayoutLiangkui Luo0https://orcid.org/0000-0003-2661-5540Zhaocheng He1https://orcid.org/0000-0002-9398-2327Yuhuan Lu2https://orcid.org/0000-0001-5332-3389Jinyong Chen3https://orcid.org/0000-0003-4697-9018School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou, ChinaSchool of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou, ChinaSchool of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou, ChinaSchool of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou, ChinaOrigin-destination (OD) flows have time-varying characteristics and spatial heterogeneity. With the increasing amount of urban travel data, it is very challenging to clarify OD flows through traditional visualization methods. A novel approach based on the force-directed edge bundling (FDEB) algorithm is proposed to visualize OD flows and identify the main corridors in a city. First, we reduce the chaos of OD flows through vertex clustering, and then we use a modified FDEB algorithm to clarify the OD flows. Furthermore, to illustrate the validity of our approach from the perspective of traffic-representation ability, we design three evaluation metrics: local feature richness(LFR) and strength of spatial relationship (SSR), which measure the ability to express the spatial heterogeneity of OD flows, and time characteristic richness (TCR), which measures the ability to express time-varying characteristics in OD flows. Experiments are conducted on real-world automatic vehicle identification (AVI) data that are gathered from Xuancheng, China. The results show that our method can well enhance the representation of spatial heterogeneity and uncover the temporal characteristics hidden in OD flows.https://ieeexplore.ieee.org/document/9046019/Edge bundlingOD flow datatraffic desire linesvisualization evaluation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Liangkui Luo Zhaocheng He Yuhuan Lu Jinyong Chen |
spellingShingle |
Liangkui Luo Zhaocheng He Yuhuan Lu Jinyong Chen Clarifying Origin-Destination Flows Using Force-Directed Edge Bundling Layout IEEE Access Edge bundling OD flow data traffic desire lines visualization evaluation |
author_facet |
Liangkui Luo Zhaocheng He Yuhuan Lu Jinyong Chen |
author_sort |
Liangkui Luo |
title |
Clarifying Origin-Destination Flows Using Force-Directed Edge Bundling Layout |
title_short |
Clarifying Origin-Destination Flows Using Force-Directed Edge Bundling Layout |
title_full |
Clarifying Origin-Destination Flows Using Force-Directed Edge Bundling Layout |
title_fullStr |
Clarifying Origin-Destination Flows Using Force-Directed Edge Bundling Layout |
title_full_unstemmed |
Clarifying Origin-Destination Flows Using Force-Directed Edge Bundling Layout |
title_sort |
clarifying origin-destination flows using force-directed edge bundling layout |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
Origin-destination (OD) flows have time-varying characteristics and spatial heterogeneity. With the increasing amount of urban travel data, it is very challenging to clarify OD flows through traditional visualization methods. A novel approach based on the force-directed edge bundling (FDEB) algorithm is proposed to visualize OD flows and identify the main corridors in a city. First, we reduce the chaos of OD flows through vertex clustering, and then we use a modified FDEB algorithm to clarify the OD flows. Furthermore, to illustrate the validity of our approach from the perspective of traffic-representation ability, we design three evaluation metrics: local feature richness(LFR) and strength of spatial relationship (SSR), which measure the ability to express the spatial heterogeneity of OD flows, and time characteristic richness (TCR), which measures the ability to express time-varying characteristics in OD flows. Experiments are conducted on real-world automatic vehicle identification (AVI) data that are gathered from Xuancheng, China. The results show that our method can well enhance the representation of spatial heterogeneity and uncover the temporal characteristics hidden in OD flows. |
topic |
Edge bundling OD flow data traffic desire lines visualization evaluation |
url |
https://ieeexplore.ieee.org/document/9046019/ |
work_keys_str_mv |
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