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  4. Analyzing the Link Relationships of the Urban Road Network for Identifying Traffic Impact Areas
 
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Analyzing the Link Relationships of the Urban Road Network for Identifying Traffic Impact Areas

Date Issued
2014
Date
2014
Author(s)
Lai, Pei-Chun
URI
http://ntur.lib.ntu.edu.tw//handle/246246/261985
Abstract
With the growing number of developing cities, traffic congestion has becomes a global issue. To resolve congestion, the first step is to identify where it occurs. Many methods to detect congestion have been developed. However, the geographic extent of the congestion has seldom been discussed. Therefore, when congestion occurs, the roads influencing the problem are unknown, and control strategies cannot be implemented to avoid further congestion. Existing guidelines for conducting traffic impact assessments to determine the extent of impact from congestion define assessment areas based on a certain distance. This study considers other factors that influence the impact area, as distance is not the only factor. This study proposes using Flow-Based PageRank (FBPR) to build link relationships between roads, where the strength of a link relationship represents the intensity of interplay. The community structures index identifies where more interplay occurs, and geographic extent illustrates areas of denser connections, which can indicate impact area. To test the robustness of FBPR, different sizes of data were used. The findings revealed that the values of link relationships were primarily distributed between 0.1 and 0.4 and indicated the existence of a tendency that regulates the flow pattern in a road network. The community structures index value was 0.94 and confirmed the existence of subgroups within the road network. The distribution of community structures was constrained by spatial and network structure, and large communities were distributed in high-volume areas. This indicates that high-volume areas would have a greater impact. In conclusion, FBPR can be used to construct link relationships between roads, and this information can be further applied to link analysis of road networks. The communities in a road network represent geographic subgroups within which interplay is higher, which provides management areas for planners.
Subjects
權重交通網絡
轉向率
遞移
PageRank
子群
衝擊區
SDGs

[SDGs]SDG9

[SDGs]SDG11

Type
thesis
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ntu-103-R01228013-1.pdf

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(MD5):9335a14d9a4ec2fc609c40b8ea09905a

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