Spatiotemporal impact assessments of highway construction: autonomous SWAT modeling

Kunhee Choi; Junseo Bae

Spatiotemporal impact assessments of highway construction: autonomous SWAT modeling

Kunhee Choi, Junseo Bae

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In the United States, the completion of Construction Work Zone (CWZ) impact assessments for all federally-funded highway infrastructure improvement projects is mandated, yet it is regarded as a daunting task for state transportation agencies, due to a lack of standardized analytical methods for developing sounder Transportation Management Plans (TMPs). To circumvent these issues, this study aims to create a spatiotemporal modeling framework, dubbed "SWAT" (Spatiotemporal Work zone Assessment for TMPs). This study drew a total of 43,795 traffic sensor reading data collected from heavily trafficked highways in U.S. metropolitan areas. A multilevel-cluster-driven analysis characterized traffic patterns, while being verified using a measurement system analysis. An artificial neural networks model was created to predict potential 24/7 traffic demand automatically, and its predictive power was statistically validated. It is proposed that the predicted traffic patterns will be then incorporated into a what-if scenario analysis that evaluates the impact of numerous alternative construction plans. This study will yield a breakthrough in automating CWZ impact assessments with the first view of a systematic estimation method.

Original language	English
Title of host publication	Proceedings of the 6th international conference on construction, engineering and project management
Editors	Bonsang Koo, Youngsoo Jung, Leen-Seok Kang
Publisher	Korea Institute of Construction Engineering and Management
Pages	294-298
Number of pages	5
Volume	6
ISBN (Print)	9788995457269
Publication status	Published - 31 Oct 2015
Event	The 6th International Conference on Construction Engineering and Project Management - Busan, Korea, Republic of Duration: 11 Oct 2015 → 14 Oct 2015

Conference

Conference	The 6th International Conference on Construction Engineering and Project Management
Abbreviated title	ICCEPM-2015
Country/Territory	Korea, Republic of
City	Busan
Period	11/10/15 → 14/10/15

Keywords

spatiotemporal modeling framework
transportation infrastructure improvement
construction work zone impact assessments
traffic demand prediction

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Cite this

Choi, K., & Bae, J. (2015). Spatiotemporal impact assessments of highway construction: autonomous SWAT modeling. In B. Koo, Y. Jung, & L.-S. Kang (Eds.), Proceedings of the 6th international conference on construction, engineering and project management (Vol. 6, pp. 294-298). Korea Institute of Construction Engineering and Management. http://www.auric.or.kr/User/Rdoc/DocRdoc.aspx?returnVal=RD_R&dn=330568#.XkZ61Gj7SM8

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title = "Spatiotemporal impact assessments of highway construction: autonomous SWAT modeling",

abstract = "In the United States, the completion of Construction Work Zone (CWZ) impact assessments for all federally-funded highway infrastructure improvement projects is mandated, yet it is regarded as a daunting task for state transportation agencies, due to a lack of standardized analytical methods for developing sounder Transportation Management Plans (TMPs). To circumvent these issues, this study aims to create a spatiotemporal modeling framework, dubbed {"}SWAT{"} (Spatiotemporal Work zone Assessment for TMPs). This study drew a total of 43,795 traffic sensor reading data collected from heavily trafficked highways in U.S. metropolitan areas. A multilevel-cluster-driven analysis characterized traffic patterns, while being verified using a measurement system analysis. An artificial neural networks model was created to predict potential 24/7 traffic demand automatically, and its predictive power was statistically validated. It is proposed that the predicted traffic patterns will be then incorporated into a what-if scenario analysis that evaluates the impact of numerous alternative construction plans. This study will yield a breakthrough in automating CWZ impact assessments with the first view of a systematic estimation method.",

keywords = "spatiotemporal modeling framework, transportation infrastructure improvement, construction work zone impact assessments, traffic demand prediction",

author = "Kunhee Choi and Junseo Bae",

year = "2015",

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isbn = "9788995457269",

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pages = "294--298",

editor = "Bonsang Koo and Youngsoo Jung and Leen-Seok Kang",

booktitle = "Proceedings of the 6th international conference on construction, engineering and project management",

publisher = "Korea Institute of Construction Engineering and Management",

address = "Korea, Republic of",

note = "The 6th International Conference on Construction Engineering and Project Management , ICCEPM-2015 ; Conference date: 11-10-2015 Through 14-10-2015",

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Choi, K & Bae, J 2015, Spatiotemporal impact assessments of highway construction: autonomous SWAT modeling. in B Koo, Y Jung & L-S Kang (eds), Proceedings of the 6th international conference on construction, engineering and project management. vol. 6, Korea Institute of Construction Engineering and Management, pp. 294-298, The 6th International Conference on Construction Engineering and Project Management , Busan, Korea, Republic of, 11/10/15. <http://www.auric.or.kr/User/Rdoc/DocRdoc.aspx?returnVal=RD_R&dn=330568#.XkZ61Gj7SM8>

Spatiotemporal impact assessments of highway construction: autonomous SWAT modeling. / Choi, Kunhee; Bae, Junseo.
Proceedings of the 6th international conference on construction, engineering and project management. ed. / Bonsang Koo; Youngsoo Jung; Leen-Seok Kang. Vol. 6 Korea Institute of Construction Engineering and Management, 2015. p. 294-298.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Spatiotemporal impact assessments of highway construction

T2 - The 6th International Conference on Construction Engineering and Project Management

AU - Choi, Kunhee

AU - Bae, Junseo

PY - 2015/10/31

Y1 - 2015/10/31

N2 - In the United States, the completion of Construction Work Zone (CWZ) impact assessments for all federally-funded highway infrastructure improvement projects is mandated, yet it is regarded as a daunting task for state transportation agencies, due to a lack of standardized analytical methods for developing sounder Transportation Management Plans (TMPs). To circumvent these issues, this study aims to create a spatiotemporal modeling framework, dubbed "SWAT" (Spatiotemporal Work zone Assessment for TMPs). This study drew a total of 43,795 traffic sensor reading data collected from heavily trafficked highways in U.S. metropolitan areas. A multilevel-cluster-driven analysis characterized traffic patterns, while being verified using a measurement system analysis. An artificial neural networks model was created to predict potential 24/7 traffic demand automatically, and its predictive power was statistically validated. It is proposed that the predicted traffic patterns will be then incorporated into a what-if scenario analysis that evaluates the impact of numerous alternative construction plans. This study will yield a breakthrough in automating CWZ impact assessments with the first view of a systematic estimation method.

AB - In the United States, the completion of Construction Work Zone (CWZ) impact assessments for all federally-funded highway infrastructure improvement projects is mandated, yet it is regarded as a daunting task for state transportation agencies, due to a lack of standardized analytical methods for developing sounder Transportation Management Plans (TMPs). To circumvent these issues, this study aims to create a spatiotemporal modeling framework, dubbed "SWAT" (Spatiotemporal Work zone Assessment for TMPs). This study drew a total of 43,795 traffic sensor reading data collected from heavily trafficked highways in U.S. metropolitan areas. A multilevel-cluster-driven analysis characterized traffic patterns, while being verified using a measurement system analysis. An artificial neural networks model was created to predict potential 24/7 traffic demand automatically, and its predictive power was statistically validated. It is proposed that the predicted traffic patterns will be then incorporated into a what-if scenario analysis that evaluates the impact of numerous alternative construction plans. This study will yield a breakthrough in automating CWZ impact assessments with the first view of a systematic estimation method.

KW - spatiotemporal modeling framework

KW - transportation infrastructure improvement

KW - construction work zone impact assessments

KW - traffic demand prediction

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M3 - Conference contribution

SN - 9788995457269

VL - 6

SP - 294

EP - 298

BT - Proceedings of the 6th international conference on construction, engineering and project management

A2 - Koo, Bonsang

A2 - Jung, Youngsoo

A2 - Kang, Leen-Seok

PB - Korea Institute of Construction Engineering and Management

Y2 - 11 October 2015 through 14 October 2015

ER -

Spatiotemporal impact assessments of highway construction: autonomous SWAT modeling

Abstract

Conference

Keywords

UN SDGs

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