Generating Near-Optimal Road Condition-Capacity Improvement Decisions Using Monte Carlo Simulations

Obunguta, Felix; Kaito, Kiyoyuki; Sasai, Kotaro; Kobayashi, Kiyoshi; Matsushima, Kakuya; Bakamwesiga, Hilary

doi:https://doi.org/10.1061/JITSE4.ISENG-2429

Generating Near-Optimal Road Condition-Capacity Improvement Decisions Using Monte Carlo Simulations

Obunguta, Felix; Kaito, Kiyoyuki; Sasai, Kotaro et al.

Journal of Infrastructure Systems, 2024, 30(4), 04024027

アクセス数:506件（2026-02-15 05:16 集計）

固定URL: https://hdl.handle.net/11094/98428

閲覧可能ファイル

ファイル	フォーマット	利用条件	サイズ	閲覧回数	利用開始日	説明	information
JInfrastructSyst_30_4_04024027	pdf	なし	1.16 MB	20	2025.12.01

論文情報

ファイル出力 EndNote Basic出力 Mendeley出力

タイトル	Generating Near-Optimal Road Condition-Capacity Improvement Decisions Using Monte Carlo Simulations
著者	ORCID 0000-0001-8145-6285 0000-0001-8145-6285 researchmap permalink felixo felixo NRID 1000051013255 1000051013255 Scopus Author ID 57221272688 57221272688 著者名 Obunguta, Felix
	NRID 1000030467550 1000030467550 researchmap permalink asset-metrics asset-metrics Scopus Author ID 7006682889 7006682889 著者名 Kaito, Kiyoyuki
	著者名 Sasai, Kotaro
	NRID 1000050115846 1000050115846 著者名 Kobayashi, Kiyoshi
	NRID 1000060303848 1000060303848 著者名 Matsushima, Kakuya
	著者名 Bakamwesiga, Hilary
内容	This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil Engineers. This material may be found at https://doi.org/10.1061/JITSE4.ISENG-2429.
抄録	Road travel cost can be defined as a function of condition and volume-capacity factors. Asset managers intervene on heavily trafficked and poor condition roads based on criteria to optimize network travel and intervention (social) costs. These criteria may involve a trade-off between improving the road condition or capacity. Road performance is known through periodic inspection and stochastic modeling to estimate a deteriorated future condition. The predicted future condition and traffic growth rates change pavement section intervention (capacity or condition improvement) priority over time. The optimal road intervention choice can be determined using algorithms, including the greedy algorithm and Monte Carlo simulations. Greedy algorithms search through the entire sample space locally and stepwise to approximate global optima, whereas Monte Carlo simulations randomly sample candidate sections to generate more globally optimum interventions. This study proposes a road asset management model using Monte Carlo methods to optimally choose road network interventions considering condition and traffic changes over a planning horizon. The study includes an empirical application using real world data and compares the proposed Monte Carlo simulations approach to the greedy algorithm.
出版者	American Society of Civil Engineers (ASCE)
収録物名	Journal of Infrastructure Systems
巻(号)	30(4)
ページ	04024027
刊行年月	2024-12-01
言語	英語
ハンドルURL	https://hdl.handle.net/11094/98428
PISSN	10760342
eISSN	1943555X
関連情報 (isVersionOf)	DOI (出版社版) https://doi.org/10.1061/JITSE4.ISENG-2429
アクセス権	open access
権利情報	© 2024 American Society of Civil Engineers
出版タイプ	Accepted Manuscript
カテゴリ	学術雑誌論文 Journal Article

資源タイプ	学術雑誌論文
ローカル資源タイプ	学術雑誌論文
DCTERMS.bibliographicCitation	Journal of Infrastructure Systems. 2024, 30(4), p. 04024027
DC.title	Generating Near-Optimal Road Condition-Capacity Improvement Decisions Using Monte Carlo Simulations
DC.creator	Obunguta, Felix
	Kaito, Kiyoyuki
	Sasai, Kotaro
	Kobayashi, Kiyoshi
	Matsushima, Kakuya
	Bakamwesiga, Hilary
DC.publisher	American Society of Civil Engineers (ASCE)
DC.language' scheme='DCTERMS.RFC1766	英語
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2024-12-01
DC.identifier	https://doi.org/10.1061/JITSE4.ISENG-2429
DC.description	This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil Engineers. This material may be found at https://doi.org/10.1061/JITSE4.ISENG-2429.
DCTERMS.abstract	Road travel cost can be defined as a function of condition and volume-capacity factors. Asset managers intervene on heavily trafficked and poor condition roads based on criteria to optimize network travel and intervention (social) costs. These criteria may involve a trade-off between improving the road condition or capacity. Road performance is known through periodic inspection and stochastic modeling to estimate a deteriorated future condition. The predicted future condition and traffic growth rates change pavement section intervention (capacity or condition improvement) priority over time. The optimal road intervention choice can be determined using algorithms, including the greedy algorithm and Monte Carlo simulations. Greedy algorithms search through the entire sample space locally and stepwise to approximate global optima, whereas Monte Carlo simulations randomly sample candidate sections to generate more globally optimum interventions. This study proposes a road asset management model using Monte Carlo methods to optimally choose road network interventions considering condition and traffic changes over a planning horizon. The study includes an empirical application using real world data and compares the proposed Monte Carlo simulations approach to the greedy algorithm.
DC.rights	© 2024 American Society of Civil Engineers
DC.format	98428
citation_title	Generating Near-Optimal Road Condition-Capacity Improvement Decisions Using Monte Carlo Simulations
citation_author	Obunguta, Felix
	Kaito, Kiyoyuki
	Sasai, Kotaro
	Kobayashi, Kiyoshi
	Matsushima, Kakuya
	Bakamwesiga, Hilary
citation_publisher	American Society of Civil Engineers (ASCE)
citation_language	英語
citation_date	2024-12-01
citation_journal_title	Journal of Infrastructure Systems
citation_volume	30
citation_issue	4
citation_firstpage	04024027
citation_doi	https://doi.org/10.1061/JITSE4.ISENG-2429
citation_public_url	https://hdl.handle.net/11094/98428

Generating Near-Optimal Road Condition-Capacity Improvement Decisions Using Monte Carlo Simulations

閲覧可能ファイル

論文情報

論文詳細を表示