Heuristic urban transportation network design method, a multilayer co-evolution approach
The design of urban transportation networks plays a key role in the urban planning process, and the coevolution of urban networks has recently garnered significant attention in literature. However, most of these recent articles are based on networks that are essentially planar. In this research, we...
Saved in:
Main Authors: | , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2017
|
Online Access: | http://psasir.upm.edu.my/id/eprint/62027/1/Heuristic%20urban%20transportation%20network%20design%20method%2C%20a%20multilayer%20coevolution%20approach.pdf http://psasir.upm.edu.my/id/eprint/62027/ https://www.sciencedirect.com/science/article/pii/S0378437117302078 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Universiti Putra Malaysia |
Language: | English |
id |
my.upm.eprints.62027 |
---|---|
record_format |
eprints |
spelling |
my.upm.eprints.620272019-03-18T09:11:28Z http://psasir.upm.edu.my/id/eprint/62027/ Heuristic urban transportation network design method, a multilayer co-evolution approach Ding, Rui Ujang, Norsidah Hamid, Hussain Abd Manan, Mohd Shahrudin Li, Rong Wu, Jianjun The design of urban transportation networks plays a key role in the urban planning process, and the coevolution of urban networks has recently garnered significant attention in literature. However, most of these recent articles are based on networks that are essentially planar. In this research, we propose a heuristic multilayer urban network coevolution model with lower layer network and upper layer network that are associated with growth and stimulate one another. We first use the relative neighbourhood graph and the Gabriel graph to simulate the structure of rail and road networks, respectively. With simulation we find that when a specific number of nodes are added, the total travel cost ratio between an expanded network and the initial lower layer network has the lowest value. The cooperation strength Λ and the changeable parameter average operation speed ratio Θ show that transit users’ route choices change dramatically through the coevolution process and that their decisions, in turn, affect the multilayer network structure. We also note that the simulated relation between the Gini coefficient of the betweenness centrality, Θ and Λ have an optimal point for network design. This research could inspire the analysis of urban network topology features and the assessment of urban growth trends. Elsevier 2017-08 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/62027/1/Heuristic%20urban%20transportation%20network%20design%20method%2C%20a%20multilayer%20coevolution%20approach.pdf Ding, Rui and Ujang, Norsidah and Hamid, Hussain and Abd Manan, Mohd Shahrudin and Li, Rong and Wu, Jianjun (2017) Heuristic urban transportation network design method, a multilayer co-evolution approach. Physica A: Statistical Mechanics and its Applications, 479. 71 - 83. ISSN 0378-4371; ESSN: 1873-2119 https://www.sciencedirect.com/science/article/pii/S0378437117302078 10.1016/j.physa.2017.02.051 |
institution |
Universiti Putra Malaysia |
building |
UPM Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Putra Malaysia |
content_source |
UPM Institutional Repository |
url_provider |
http://psasir.upm.edu.my/ |
language |
English |
description |
The design of urban transportation networks plays a key role in the urban planning process, and the coevolution of urban networks has recently garnered significant attention in literature. However, most of these recent articles are based on networks that are essentially planar. In this research, we propose a heuristic multilayer urban network coevolution model with lower layer network and upper layer network that are associated with growth and stimulate one another. We first use the relative neighbourhood graph and the Gabriel graph to simulate the structure of rail and road networks, respectively. With simulation we find that when a specific number of nodes are added, the total travel cost ratio between an expanded network and the initial lower layer network has the lowest value. The cooperation strength Λ and the changeable parameter average operation speed ratio Θ show that transit users’ route choices change dramatically through the coevolution process and that their decisions, in turn, affect the multilayer network structure. We also note that the simulated relation between the Gini coefficient of the betweenness centrality, Θ and Λ have an optimal point for network design. This research could inspire the analysis of urban network topology features and the assessment of urban growth trends. |
format |
Article |
author |
Ding, Rui Ujang, Norsidah Hamid, Hussain Abd Manan, Mohd Shahrudin Li, Rong Wu, Jianjun |
spellingShingle |
Ding, Rui Ujang, Norsidah Hamid, Hussain Abd Manan, Mohd Shahrudin Li, Rong Wu, Jianjun Heuristic urban transportation network design method, a multilayer co-evolution approach |
author_facet |
Ding, Rui Ujang, Norsidah Hamid, Hussain Abd Manan, Mohd Shahrudin Li, Rong Wu, Jianjun |
author_sort |
Ding, Rui |
title |
Heuristic urban transportation network design method, a multilayer co-evolution approach |
title_short |
Heuristic urban transportation network design method, a multilayer co-evolution approach |
title_full |
Heuristic urban transportation network design method, a multilayer co-evolution approach |
title_fullStr |
Heuristic urban transportation network design method, a multilayer co-evolution approach |
title_full_unstemmed |
Heuristic urban transportation network design method, a multilayer co-evolution approach |
title_sort |
heuristic urban transportation network design method, a multilayer co-evolution approach |
publisher |
Elsevier |
publishDate |
2017 |
url |
http://psasir.upm.edu.my/id/eprint/62027/1/Heuristic%20urban%20transportation%20network%20design%20method%2C%20a%20multilayer%20coevolution%20approach.pdf http://psasir.upm.edu.my/id/eprint/62027/ https://www.sciencedirect.com/science/article/pii/S0378437117302078 |
_version_ |
1643837651304841216 |