The Experts below are selected from a list of 42165 Experts worldwide ranked by ideXlab platform
Junqing Tang - One of the best experts on this subject based on the ideXlab platform.
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evaluating resilience in urban transportation systems for sustainability a systems based Bayesian Network Model
Transportation Research Part C-emerging Technologies, 2019Co-Authors: Junqing Tang, Hans Rudolf Heinimann, Ke Han, Hanbin Luo, Botao ZhongAbstract:Abstract This paper proposes a hierarchical Bayesian Network Model (BNM) to quantitatively evaluate the resilience of urban transportation systems. Based on systemic thinking and taking a sustainability perspective, we investigate the long-term resilience of the road transportation systems in four cities in China from 1998 to 2017, namely Beijing, Tianjin, Shanghai, and Chongqing, respectively. The Model takes into account various factors collected from multi-source data platforms involved in stages of design, construction, operation, management, and innovation in road transportation systems. We test the Model with the forward inference, sensitivity analysis, and backward inference. The result shows that the overall resilience scores of all four cities’ transportation systems are within a moderate range with values between 49% to 59%. Although they all have an ever-increasing economic level, the levels of transportation resilience in Beijing and Tianjin decrease first and then gradually increase in a long run, which indicates a strong multi-dimensional, dynamic, and non-linear characteristic in resilience-economic coupling effect. Additionally, the results obtained from the sensitivity analysis and backward inference suggest that decision makers should pay more attention to the capabilities of quickly rebuilding and making changes to cope with future disturbances. As an exploratory study, this study clarifies the concepts of long-term multi-dimensional resilience and specific hazard-related resilience and provides an effective decision-support tool for stakeholders when building resilient infrastructure.
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evaluating resilience in urban transportation systems for sustainability a systems based Bayesian Network Model
arXiv: Physics and Society, 2019Co-Authors: Junqing Tang, Hans Rudolf Heinimann, Ke Han, Hanbin Luo, Botao ZhongAbstract:This paper proposes a hierarchical Bayesian Network Model (BNM) to quantitatively evaluate the resilience of urban transportation infrastructure. Based on systemic thinkings and sustainability perspectives, we investigate the long-term resilience of the road transportation systems in four cities of China from 1998 to 2017, namely Beijing, Tianjin, Shanghai, and Chongqing, respectively. The Model takes into account the factors involved in stages of design, construction, operation, management, and innovation of urban road transportation, which collected from multi-source data platforms. We test the Model with the forward inference, sensitivity analysis, and backward inference. The result shows that the overall resilience of all four cities' transportation infrastructure is within a moderate range with values between 50% to 60%. Although they all have an ever-increasing economic level, Beijing and Tianjin demonstrate a clear "V" shape in the long-term transportation resilience, which indicates a strong multi-dimensional, dynamic, and non-linear characteristic in resilience-economic coupling effect. Additionally, the results obtained from the sensitivity analysis and backward inference suggest that urban decision-makers should pay more attention to the capabilities of quick rebuilding and making changes to cope with future disturbance. As an exploratory study, this study clarifies the concepts of long-term multi-dimensional resilience and specific hazard-related resilience and provides an effective decision-support tool for stakeholders when building sustainable infrastructure.
Botao Zhong - One of the best experts on this subject based on the ideXlab platform.
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evaluating resilience in urban transportation systems for sustainability a systems based Bayesian Network Model
Transportation Research Part C-emerging Technologies, 2019Co-Authors: Junqing Tang, Hans Rudolf Heinimann, Ke Han, Hanbin Luo, Botao ZhongAbstract:Abstract This paper proposes a hierarchical Bayesian Network Model (BNM) to quantitatively evaluate the resilience of urban transportation systems. Based on systemic thinking and taking a sustainability perspective, we investigate the long-term resilience of the road transportation systems in four cities in China from 1998 to 2017, namely Beijing, Tianjin, Shanghai, and Chongqing, respectively. The Model takes into account various factors collected from multi-source data platforms involved in stages of design, construction, operation, management, and innovation in road transportation systems. We test the Model with the forward inference, sensitivity analysis, and backward inference. The result shows that the overall resilience scores of all four cities’ transportation systems are within a moderate range with values between 49% to 59%. Although they all have an ever-increasing economic level, the levels of transportation resilience in Beijing and Tianjin decrease first and then gradually increase in a long run, which indicates a strong multi-dimensional, dynamic, and non-linear characteristic in resilience-economic coupling effect. Additionally, the results obtained from the sensitivity analysis and backward inference suggest that decision makers should pay more attention to the capabilities of quickly rebuilding and making changes to cope with future disturbances. As an exploratory study, this study clarifies the concepts of long-term multi-dimensional resilience and specific hazard-related resilience and provides an effective decision-support tool for stakeholders when building resilient infrastructure.
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evaluating resilience in urban transportation systems for sustainability a systems based Bayesian Network Model
arXiv: Physics and Society, 2019Co-Authors: Junqing Tang, Hans Rudolf Heinimann, Ke Han, Hanbin Luo, Botao ZhongAbstract:This paper proposes a hierarchical Bayesian Network Model (BNM) to quantitatively evaluate the resilience of urban transportation infrastructure. Based on systemic thinkings and sustainability perspectives, we investigate the long-term resilience of the road transportation systems in four cities of China from 1998 to 2017, namely Beijing, Tianjin, Shanghai, and Chongqing, respectively. The Model takes into account the factors involved in stages of design, construction, operation, management, and innovation of urban road transportation, which collected from multi-source data platforms. We test the Model with the forward inference, sensitivity analysis, and backward inference. The result shows that the overall resilience of all four cities' transportation infrastructure is within a moderate range with values between 50% to 60%. Although they all have an ever-increasing economic level, Beijing and Tianjin demonstrate a clear "V" shape in the long-term transportation resilience, which indicates a strong multi-dimensional, dynamic, and non-linear characteristic in resilience-economic coupling effect. Additionally, the results obtained from the sensitivity analysis and backward inference suggest that urban decision-makers should pay more attention to the capabilities of quick rebuilding and making changes to cope with future disturbance. As an exploratory study, this study clarifies the concepts of long-term multi-dimensional resilience and specific hazard-related resilience and provides an effective decision-support tool for stakeholders when building sustainable infrastructure.
Ke Han - One of the best experts on this subject based on the ideXlab platform.
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evaluating resilience in urban transportation systems for sustainability a systems based Bayesian Network Model
Transportation Research Part C-emerging Technologies, 2019Co-Authors: Junqing Tang, Hans Rudolf Heinimann, Ke Han, Hanbin Luo, Botao ZhongAbstract:Abstract This paper proposes a hierarchical Bayesian Network Model (BNM) to quantitatively evaluate the resilience of urban transportation systems. Based on systemic thinking and taking a sustainability perspective, we investigate the long-term resilience of the road transportation systems in four cities in China from 1998 to 2017, namely Beijing, Tianjin, Shanghai, and Chongqing, respectively. The Model takes into account various factors collected from multi-source data platforms involved in stages of design, construction, operation, management, and innovation in road transportation systems. We test the Model with the forward inference, sensitivity analysis, and backward inference. The result shows that the overall resilience scores of all four cities’ transportation systems are within a moderate range with values between 49% to 59%. Although they all have an ever-increasing economic level, the levels of transportation resilience in Beijing and Tianjin decrease first and then gradually increase in a long run, which indicates a strong multi-dimensional, dynamic, and non-linear characteristic in resilience-economic coupling effect. Additionally, the results obtained from the sensitivity analysis and backward inference suggest that decision makers should pay more attention to the capabilities of quickly rebuilding and making changes to cope with future disturbances. As an exploratory study, this study clarifies the concepts of long-term multi-dimensional resilience and specific hazard-related resilience and provides an effective decision-support tool for stakeholders when building resilient infrastructure.
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evaluating resilience in urban transportation systems for sustainability a systems based Bayesian Network Model
arXiv: Physics and Society, 2019Co-Authors: Junqing Tang, Hans Rudolf Heinimann, Ke Han, Hanbin Luo, Botao ZhongAbstract:This paper proposes a hierarchical Bayesian Network Model (BNM) to quantitatively evaluate the resilience of urban transportation infrastructure. Based on systemic thinkings and sustainability perspectives, we investigate the long-term resilience of the road transportation systems in four cities of China from 1998 to 2017, namely Beijing, Tianjin, Shanghai, and Chongqing, respectively. The Model takes into account the factors involved in stages of design, construction, operation, management, and innovation of urban road transportation, which collected from multi-source data platforms. We test the Model with the forward inference, sensitivity analysis, and backward inference. The result shows that the overall resilience of all four cities' transportation infrastructure is within a moderate range with values between 50% to 60%. Although they all have an ever-increasing economic level, Beijing and Tianjin demonstrate a clear "V" shape in the long-term transportation resilience, which indicates a strong multi-dimensional, dynamic, and non-linear characteristic in resilience-economic coupling effect. Additionally, the results obtained from the sensitivity analysis and backward inference suggest that urban decision-makers should pay more attention to the capabilities of quick rebuilding and making changes to cope with future disturbance. As an exploratory study, this study clarifies the concepts of long-term multi-dimensional resilience and specific hazard-related resilience and provides an effective decision-support tool for stakeholders when building sustainable infrastructure.
Ole-christoffer Granmo - One of the best experts on this subject based on the ideXlab platform.
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MOD - A Bayesian Network Model for Fire Assessment and Prediction
Lecture Notes in Computer Science, 2015Co-Authors: Mehdi Ben Lazreg, Jaziar Radianti, Ole-christoffer GranmoAbstract:Smartphones and other wearable computers with modern sensor technologies are becoming more advanced and widespread. This paper proposes exploiting those devices to help the firefighting operation. It introduces a Bayesian Network Model that infers the state of the fire and predicts its future development based on smartphone sensor data gathered within the fire area. The Model provides a prediction accuracy of 84.79i¾?% and an area under the curve of 0.83. This solution had also been tested in the context of a fire drill and proved to help firefighters assess the fire situation and speed up their work.
Hanbin Luo - One of the best experts on this subject based on the ideXlab platform.
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evaluating resilience in urban transportation systems for sustainability a systems based Bayesian Network Model
Transportation Research Part C-emerging Technologies, 2019Co-Authors: Junqing Tang, Hans Rudolf Heinimann, Ke Han, Hanbin Luo, Botao ZhongAbstract:Abstract This paper proposes a hierarchical Bayesian Network Model (BNM) to quantitatively evaluate the resilience of urban transportation systems. Based on systemic thinking and taking a sustainability perspective, we investigate the long-term resilience of the road transportation systems in four cities in China from 1998 to 2017, namely Beijing, Tianjin, Shanghai, and Chongqing, respectively. The Model takes into account various factors collected from multi-source data platforms involved in stages of design, construction, operation, management, and innovation in road transportation systems. We test the Model with the forward inference, sensitivity analysis, and backward inference. The result shows that the overall resilience scores of all four cities’ transportation systems are within a moderate range with values between 49% to 59%. Although they all have an ever-increasing economic level, the levels of transportation resilience in Beijing and Tianjin decrease first and then gradually increase in a long run, which indicates a strong multi-dimensional, dynamic, and non-linear characteristic in resilience-economic coupling effect. Additionally, the results obtained from the sensitivity analysis and backward inference suggest that decision makers should pay more attention to the capabilities of quickly rebuilding and making changes to cope with future disturbances. As an exploratory study, this study clarifies the concepts of long-term multi-dimensional resilience and specific hazard-related resilience and provides an effective decision-support tool for stakeholders when building resilient infrastructure.
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evaluating resilience in urban transportation systems for sustainability a systems based Bayesian Network Model
arXiv: Physics and Society, 2019Co-Authors: Junqing Tang, Hans Rudolf Heinimann, Ke Han, Hanbin Luo, Botao ZhongAbstract:This paper proposes a hierarchical Bayesian Network Model (BNM) to quantitatively evaluate the resilience of urban transportation infrastructure. Based on systemic thinkings and sustainability perspectives, we investigate the long-term resilience of the road transportation systems in four cities of China from 1998 to 2017, namely Beijing, Tianjin, Shanghai, and Chongqing, respectively. The Model takes into account the factors involved in stages of design, construction, operation, management, and innovation of urban road transportation, which collected from multi-source data platforms. We test the Model with the forward inference, sensitivity analysis, and backward inference. The result shows that the overall resilience of all four cities' transportation infrastructure is within a moderate range with values between 50% to 60%. Although they all have an ever-increasing economic level, Beijing and Tianjin demonstrate a clear "V" shape in the long-term transportation resilience, which indicates a strong multi-dimensional, dynamic, and non-linear characteristic in resilience-economic coupling effect. Additionally, the results obtained from the sensitivity analysis and backward inference suggest that urban decision-makers should pay more attention to the capabilities of quick rebuilding and making changes to cope with future disturbance. As an exploratory study, this study clarifies the concepts of long-term multi-dimensional resilience and specific hazard-related resilience and provides an effective decision-support tool for stakeholders when building sustainable infrastructure.
