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A resilience-oriented approach for quantitatively assessing recurrent spatial-temporal congestion on urban roads
Traffic congestion brings not only delay and inconvenience, but other associated national concerns, such as greenhouse gases, air pollutants, road safety issues and risks. Identification, measurement, tracking, and control of urban recurrent congestion are vital for building a livable and smart comm...
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| Published in: | PloS one 2018-01, Vol.13 (1), p.e0190616-e0190616 |
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| creator | Tang, Junqing Heinimann, Hans Rudolf |
| description | Traffic congestion brings not only delay and inconvenience, but other associated national concerns, such as greenhouse gases, air pollutants, road safety issues and risks. Identification, measurement, tracking, and control of urban recurrent congestion are vital for building a livable and smart community. A considerable amount of works has made contributions to tackle the problem. Several methods, such as time-based approaches and level of service, can be effective for characterizing congestion on urban streets. However, studies with systemic perspectives have been minor in congestion quantification. Resilience, on the other hand, is an emerging concept that focuses on comprehensive systemic performance and characterizes the ability of a system to cope with disturbance and to recover its functionality. In this paper, we symbolized recurrent congestion as internal disturbance and proposed a modified metric inspired by the well-applied "R4" resilience-triangle framework. We constructed the metric with generic dimensions from both resilience engineering and transport science to quantify recurrent congestion based on spatial-temporal traffic patterns and made the comparison with other two approaches in freeway and signal-controlled arterial cases. Results showed that the metric can effectively capture congestion patterns in the study area and provides a quantitative benchmark for comparison. Also, it suggested not only a good comparative performance in measuring strength of proposed metric, but also its capability of considering the discharging process in congestion. The sensitivity tests showed that proposed metric possesses robustness against parameter perturbation in Robustness Range (RR), but the number of identified congestion patterns can be influenced by the existence of ϵ. In addition, the Elasticity Threshold (ET) and the spatial dimension of cell-based platform differ the congestion results significantly on both the detected number and intensity. By tackling this conventional problem with emerging concept, our metric provides a systemic alternative approach and enriches the toolbox for congestion assessment. Future work will be conducted on a larger scale with multiplex scenarios in various traffic conditions. |
| doi_str_mv | 10.1371/journal.pone.0190616 |
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Identification, measurement, tracking, and control of urban recurrent congestion are vital for building a livable and smart community. A considerable amount of works has made contributions to tackle the problem. Several methods, such as time-based approaches and level of service, can be effective for characterizing congestion on urban streets. However, studies with systemic perspectives have been minor in congestion quantification. Resilience, on the other hand, is an emerging concept that focuses on comprehensive systemic performance and characterizes the ability of a system to cope with disturbance and to recover its functionality. In this paper, we symbolized recurrent congestion as internal disturbance and proposed a modified metric inspired by the well-applied "R4" resilience-triangle framework. We constructed the metric with generic dimensions from both resilience engineering and transport science to quantify recurrent congestion based on spatial-temporal traffic patterns and made the comparison with other two approaches in freeway and signal-controlled arterial cases. Results showed that the metric can effectively capture congestion patterns in the study area and provides a quantitative benchmark for comparison. Also, it suggested not only a good comparative performance in measuring strength of proposed metric, but also its capability of considering the discharging process in congestion. The sensitivity tests showed that proposed metric possesses robustness against parameter perturbation in Robustness Range (RR), but the number of identified congestion patterns can be influenced by the existence of ϵ. In addition, the Elasticity Threshold (ET) and the spatial dimension of cell-based platform differ the congestion results significantly on both the detected number and intensity. By tackling this conventional problem with emerging concept, our metric provides a systemic alternative approach and enriches the toolbox for congestion assessment. Future work will be conducted on a larger scale with multiplex scenarios in various traffic conditions.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0190616</identifier><identifier>PMID: 29293686</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Air pollution ; Analysis ; Biology and Life Sciences ; Civil engineering ; Control ; Datasets ; Disturbance ; Documentation ; Driving conditions ; Earth Sciences ; Ecology and Environmental Sciences ; Elasticity ; Engineering and Technology ; Environmental risk ; Greenhouse effect ; Greenhouse gases ; Metadata ; Methods ; Multiplexing ; Outdoor air quality ; Parameter identification ; Parameter robustness ; Parameter sensitivity ; Phase transitions ; Physical Sciences ; Pollutants ; Research and Analysis Methods ; Resilience ; Risk assessment ; Roads & highways ; Robustness ; Safety engineering ; Statistical analysis ; Streets ; Studies ; Tracking control ; Traffic ; Traffic accidents & safety ; Traffic congestion ; Traffic delay ; Traffic flow ; Transportation planning ; Vehicles</subject><ispartof>PloS one, 2018-01, Vol.13 (1), p.e0190616-e0190616</ispartof><rights>COPYRIGHT 2018 Public Library of Science</rights><rights>2018 Tang, Heinimann. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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Identification, measurement, tracking, and control of urban recurrent congestion are vital for building a livable and smart community. A considerable amount of works has made contributions to tackle the problem. Several methods, such as time-based approaches and level of service, can be effective for characterizing congestion on urban streets. However, studies with systemic perspectives have been minor in congestion quantification. Resilience, on the other hand, is an emerging concept that focuses on comprehensive systemic performance and characterizes the ability of a system to cope with disturbance and to recover its functionality. In this paper, we symbolized recurrent congestion as internal disturbance and proposed a modified metric inspired by the well-applied "R4" resilience-triangle framework. We constructed the metric with generic dimensions from both resilience engineering and transport science to quantify recurrent congestion based on spatial-temporal traffic patterns and made the comparison with other two approaches in freeway and signal-controlled arterial cases. Results showed that the metric can effectively capture congestion patterns in the study area and provides a quantitative benchmark for comparison. Also, it suggested not only a good comparative performance in measuring strength of proposed metric, but also its capability of considering the discharging process in congestion. The sensitivity tests showed that proposed metric possesses robustness against parameter perturbation in Robustness Range (RR), but the number of identified congestion patterns can be influenced by the existence of ϵ. In addition, the Elasticity Threshold (ET) and the spatial dimension of cell-based platform differ the congestion results significantly on both the detected number and intensity. By tackling this conventional problem with emerging concept, our metric provides a systemic alternative approach and enriches the toolbox for congestion assessment. Future work will be conducted on a larger scale with multiplex scenarios in various traffic conditions.</description><subject>Air pollution</subject><subject>Analysis</subject><subject>Biology and Life Sciences</subject><subject>Civil engineering</subject><subject>Control</subject><subject>Datasets</subject><subject>Disturbance</subject><subject>Documentation</subject><subject>Driving conditions</subject><subject>Earth Sciences</subject><subject>Ecology and Environmental Sciences</subject><subject>Elasticity</subject><subject>Engineering and Technology</subject><subject>Environmental risk</subject><subject>Greenhouse effect</subject><subject>Greenhouse gases</subject><subject>Metadata</subject><subject>Methods</subject><subject>Multiplexing</subject><subject>Outdoor air quality</subject><subject>Parameter identification</subject><subject>Parameter robustness</subject><subject>Parameter sensitivity</subject><subject>Phase transitions</subject><subject>Physical 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One</addtitle><date>2018-01-02</date><risdate>2018</risdate><volume>13</volume><issue>1</issue><spage>e0190616</spage><epage>e0190616</epage><pages>e0190616-e0190616</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><notes>ObjectType-Article-1</notes><notes>SourceType-Scholarly Journals-1</notes><notes>ObjectType-Feature-2</notes><notes>content type line 23</notes><notes>Competing Interests: The authors have declared that no competing interests exist.</notes><abstract>Traffic congestion brings not only delay and inconvenience, but other associated national concerns, such as greenhouse gases, air pollutants, road safety issues and risks. Identification, measurement, tracking, and control of urban recurrent congestion are vital for building a livable and smart community. A considerable amount of works has made contributions to tackle the problem. Several methods, such as time-based approaches and level of service, can be effective for characterizing congestion on urban streets. However, studies with systemic perspectives have been minor in congestion quantification. Resilience, on the other hand, is an emerging concept that focuses on comprehensive systemic performance and characterizes the ability of a system to cope with disturbance and to recover its functionality. In this paper, we symbolized recurrent congestion as internal disturbance and proposed a modified metric inspired by the well-applied "R4" resilience-triangle framework. We constructed the metric with generic dimensions from both resilience engineering and transport science to quantify recurrent congestion based on spatial-temporal traffic patterns and made the comparison with other two approaches in freeway and signal-controlled arterial cases. Results showed that the metric can effectively capture congestion patterns in the study area and provides a quantitative benchmark for comparison. Also, it suggested not only a good comparative performance in measuring strength of proposed metric, but also its capability of considering the discharging process in congestion. The sensitivity tests showed that proposed metric possesses robustness against parameter perturbation in Robustness Range (RR), but the number of identified congestion patterns can be influenced by the existence of ϵ. In addition, the Elasticity Threshold (ET) and the spatial dimension of cell-based platform differ the congestion results significantly on both the detected number and intensity. By tackling this conventional problem with emerging concept, our metric provides a systemic alternative approach and enriches the toolbox for congestion assessment. Future work will be conducted on a larger scale with multiplex scenarios in various traffic conditions.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>29293686</pmid><doi>10.1371/journal.pone.0190616</doi><tpages>e0190616</tpages><orcidid>https://orcid.org/0000-0003-3343-8132</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Air pollution Analysis Biology and Life Sciences Civil engineering Control Datasets Disturbance Documentation Driving conditions Earth Sciences Ecology and Environmental Sciences Elasticity Engineering and Technology Environmental risk Greenhouse effect Greenhouse gases Metadata Methods Multiplexing Outdoor air quality Parameter identification Parameter robustness Parameter sensitivity Phase transitions Physical Sciences Pollutants Research and Analysis Methods Resilience Risk assessment Roads & highways Robustness Safety engineering Statistical analysis Streets Studies Tracking control Traffic Traffic accidents & safety Traffic congestion Traffic delay Traffic flow Transportation planning Vehicles |
| title | A resilience-oriented approach for quantitatively assessing recurrent spatial-temporal congestion on urban roads |
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