The Experts below are selected from a list of 19638 Experts worldwide ranked by ideXlab platform
Chaozhe Jiang - One of the best experts on this subject based on the ideXlab platform.
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nonlinear decision rule approach for real time traffic signal control for congestion and emission mitigation
Networks and Spatial Economics, 2020Co-Authors: Junwoo Song, Ke Han, Chaozhe JiangAbstract:We propose a real-time signal control framework based on a nonlinear decision rule (NDR), which defines a nonlinear mapping between Network states and signal control parameters to actual signal controls based on prevailing traffic conditions, and such a mapping is optimized via off-line simulation. The NDR is instantiated with two neural Networks: feedforward neural Network (FFNN) and recurrent neural Network (RNN), which have different ways of processing traffic information in the near past, and are compared in terms of their performances. The NDR is implemented within a microscopic traffic simulation (S-Paramics) for a real-world Network in West Glasgow, where the off-line training of the NDR amounts to a simulation-based optimization aiming to reduce delay, CO2 and black carbon emissions. The emission calculations are based on the high-fidelity vehicle dynamics generated by the simulation, and the AIRE instantaneous emission model. Extensive tests are performed to assess the NDR framework, not only in terms of its effectiveness in reducing the aforementioned objectives, but also in relation to local vs. global benefits, trade-off between delay and emissions, impact of sensor locations, and different levels of Network Saturation. The results suggest that the NDR is an effective, flexible and robust way of alleviating congestion and reducing traffic emissions.
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nonlinear decision rule approach for real time traffic signal control for congestion and emission reductions
arXiv: Optimization and Control, 2018Co-Authors: Junwoo Song, Ke Han, Chaozhe JiangAbstract:We propose a real-time signal control framework based on a nonlinear decision rule (NDR), which defines a nonlinear mapping between Network states and signal control parameters to actual signal controls based on prevailing traffic conditions, and such a mapping is optimized via off-line simulation. The NDR is instantiated with two neural Networks: feedforward neural Network (FFNN) and recurrent neural Network (RNN), which have different ways of processing traffic information in the near past, and are compared in terms of their performances. The NDR is implemented within a microscopic traffic simulation (S-Paramics) for a real-world Network in West Glasgow, where the off-line training of the NDR amounts to a simulation-based optimization aiming to reduce delay, CO2 and black carbon emissions. The emission calculations are based on the high-fidelity vehicle dynamics generated by the simulation, and the AIRE instantaneous emission model. Extensive tests are performed to assess the NDR framework, not only in terms of its effectiveness in reducing the aforementioned objectives, but also in relation to local vs. global benefits, trade-off between delay and emissions, impact of sensor locations, and different levels of Network Saturation. The results suggest that the NDR is an effective, flexible and robust way of alleviating congestion and reducing traffic emissions.
Ke Han - One of the best experts on this subject based on the ideXlab platform.
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nonlinear decision rule approach for real time traffic signal control for congestion and emission mitigation
Networks and Spatial Economics, 2020Co-Authors: Junwoo Song, Ke Han, Chaozhe JiangAbstract:We propose a real-time signal control framework based on a nonlinear decision rule (NDR), which defines a nonlinear mapping between Network states and signal control parameters to actual signal controls based on prevailing traffic conditions, and such a mapping is optimized via off-line simulation. The NDR is instantiated with two neural Networks: feedforward neural Network (FFNN) and recurrent neural Network (RNN), which have different ways of processing traffic information in the near past, and are compared in terms of their performances. The NDR is implemented within a microscopic traffic simulation (S-Paramics) for a real-world Network in West Glasgow, where the off-line training of the NDR amounts to a simulation-based optimization aiming to reduce delay, CO2 and black carbon emissions. The emission calculations are based on the high-fidelity vehicle dynamics generated by the simulation, and the AIRE instantaneous emission model. Extensive tests are performed to assess the NDR framework, not only in terms of its effectiveness in reducing the aforementioned objectives, but also in relation to local vs. global benefits, trade-off between delay and emissions, impact of sensor locations, and different levels of Network Saturation. The results suggest that the NDR is an effective, flexible and robust way of alleviating congestion and reducing traffic emissions.
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nonlinear decision rule approach for real time traffic signal control for congestion and emission reductions
arXiv: Optimization and Control, 2018Co-Authors: Junwoo Song, Ke Han, Chaozhe JiangAbstract:We propose a real-time signal control framework based on a nonlinear decision rule (NDR), which defines a nonlinear mapping between Network states and signal control parameters to actual signal controls based on prevailing traffic conditions, and such a mapping is optimized via off-line simulation. The NDR is instantiated with two neural Networks: feedforward neural Network (FFNN) and recurrent neural Network (RNN), which have different ways of processing traffic information in the near past, and are compared in terms of their performances. The NDR is implemented within a microscopic traffic simulation (S-Paramics) for a real-world Network in West Glasgow, where the off-line training of the NDR amounts to a simulation-based optimization aiming to reduce delay, CO2 and black carbon emissions. The emission calculations are based on the high-fidelity vehicle dynamics generated by the simulation, and the AIRE instantaneous emission model. Extensive tests are performed to assess the NDR framework, not only in terms of its effectiveness in reducing the aforementioned objectives, but also in relation to local vs. global benefits, trade-off between delay and emissions, impact of sensor locations, and different levels of Network Saturation. The results suggest that the NDR is an effective, flexible and robust way of alleviating congestion and reducing traffic emissions.
Junwoo Song - One of the best experts on this subject based on the ideXlab platform.
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nonlinear decision rule approach for real time traffic signal control for congestion and emission mitigation
Networks and Spatial Economics, 2020Co-Authors: Junwoo Song, Ke Han, Chaozhe JiangAbstract:We propose a real-time signal control framework based on a nonlinear decision rule (NDR), which defines a nonlinear mapping between Network states and signal control parameters to actual signal controls based on prevailing traffic conditions, and such a mapping is optimized via off-line simulation. The NDR is instantiated with two neural Networks: feedforward neural Network (FFNN) and recurrent neural Network (RNN), which have different ways of processing traffic information in the near past, and are compared in terms of their performances. The NDR is implemented within a microscopic traffic simulation (S-Paramics) for a real-world Network in West Glasgow, where the off-line training of the NDR amounts to a simulation-based optimization aiming to reduce delay, CO2 and black carbon emissions. The emission calculations are based on the high-fidelity vehicle dynamics generated by the simulation, and the AIRE instantaneous emission model. Extensive tests are performed to assess the NDR framework, not only in terms of its effectiveness in reducing the aforementioned objectives, but also in relation to local vs. global benefits, trade-off between delay and emissions, impact of sensor locations, and different levels of Network Saturation. The results suggest that the NDR is an effective, flexible and robust way of alleviating congestion and reducing traffic emissions.
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nonlinear decision rule approach for real time traffic signal control for congestion and emission reductions
arXiv: Optimization and Control, 2018Co-Authors: Junwoo Song, Ke Han, Chaozhe JiangAbstract:We propose a real-time signal control framework based on a nonlinear decision rule (NDR), which defines a nonlinear mapping between Network states and signal control parameters to actual signal controls based on prevailing traffic conditions, and such a mapping is optimized via off-line simulation. The NDR is instantiated with two neural Networks: feedforward neural Network (FFNN) and recurrent neural Network (RNN), which have different ways of processing traffic information in the near past, and are compared in terms of their performances. The NDR is implemented within a microscopic traffic simulation (S-Paramics) for a real-world Network in West Glasgow, where the off-line training of the NDR amounts to a simulation-based optimization aiming to reduce delay, CO2 and black carbon emissions. The emission calculations are based on the high-fidelity vehicle dynamics generated by the simulation, and the AIRE instantaneous emission model. Extensive tests are performed to assess the NDR framework, not only in terms of its effectiveness in reducing the aforementioned objectives, but also in relation to local vs. global benefits, trade-off between delay and emissions, impact of sensor locations, and different levels of Network Saturation. The results suggest that the NDR is an effective, flexible and robust way of alleviating congestion and reducing traffic emissions.
Mick Power - One of the best experts on this subject based on the ideXlab platform.
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the validity of the instrument to evaluate social Network in the ageing population the collaborative research on ageing in europe social Network index
WOS, 2014Co-Authors: Katarzyna Zawisza, Aleksander Galas, Beata Tobiaszadamczyk, Somnath Chatterji, Josep Maria Haro, Marta Miret, Seppo Koskinen, Mick PowerAbstract:The aim of the study was to create a simplified, easy implementable multidimensional instrument to assess all relevant elements of the structure and function of social Network within individuals across different European countries and to provide the tool for health professionals and policy makers. The analysis was based on the sample of 10 446 non-institutionalized adult population from Finland, Poland and Spain. The Social Network Questionnaire Collaborative Research on Ageing in Europe Social Network Index (COURAGE-SNI) was part of the COURAGE questionnaire. The indicators of the functioning of social Network ties (close relations), frequency of direct contact and general support were evaluated. Functions were assess within the main structural components as spouse, parents, children, grandchildren, other relatives, friends, coworkers and neighbours. The exploratory factor analysis revealed five main latent components of social Network with one component composed of hierarchical part. The confirmatory factor analysis provided an acceptable fit for the model. The generalize partial credit model was used to calculate factor scores for five components of the COURAGE-SNI considering the social Networks of ‘spouse/partner’, ‘parents’, ‘other family members’, ‘neighbours’ and ‘friends and co-workers’. The scores for every component were recalculated so as to provide the social Network Saturation ranged from 0 (the lowest) to 100% (the highest possible). Finally, the COURAGE-SNI score was obtained as the sum of weighted information calculated by the item response theory procedure for every aforementioned component. In summary, the COURAGE-SNI showed good reliability and content validity and seems to be a promising tool for the assessment of the social Network phenomenon across European countries. Copyright © 2013 John Wiley & Sons, Ltd. Key Practitioner Message The Courage-SNI is a new tool to assess the construct of social Network in population studies. The Courage-SNI is an instrument useful to identify high risk groups or populations whose social Network is poorer.
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the validity of the instrument to evaluate social Network in the ageing population the collaborative research on ageing in europe social Network index
Clinical Psychology & Psychotherapy, 2014Co-Authors: Katarzyna Zawisza, Aleksander Galas, Beata Tobiaszadamczyk, Somnath Chatterji, Josep Maria Haro, Marta Miret, Seppo Koskinen, Mick PowerAbstract:The aim of the study was to create a simplified, easy implementable multidimensional instrument to assess all relevant elements of the structure and function of social Network within individuals across different European countries and to provide the tool for health professionals and policy makers. The analysis was based on the sample of 10 446 non-institutionalized adult population from Finland, Poland and Spain. The Social Network Questionnaire Collaborative Research on Ageing in Europe Social Network Index (COURAGE-SNI) was part of the COURAGE questionnaire. The indicators of the functioning of social Network ties (close relations), frequency of direct contact and general support were evaluated. Functions were assess within the main structural components as spouse, parents, children, grandchildren, other relatives, friends, coworkers and neighbours. The exploratory factor analysis revealed five main latent components of social Network with one component composed of hierarchical part. The confirmatory factor analysis provided an acceptable fit for the model. The generalize partial credit model was used to calculate factor scores for five components of the COURAGE-SNI considering the social Networks of ‘spouse/partner’, ‘parents’, ‘other family members’, ‘neighbours’ and ‘friends and co-workers’. The scores for every component were recalculated so as to provide the social Network Saturation ranged from 0 (the lowest) to 100% (the highest possible). Finally, the COURAGE-SNI score was obtained as the sum of weighted information calculated by the item response theory procedure for every aforementioned component. In summary, the COURAGE-SNI showed good reliability and content validity and seems to be a promising tool for the assessment of the social Network phenomenon across European countries. Copyright © 2013 John Wiley & Sons, Ltd. Key Practitioner Message The Courage-SNI is a new tool to assess the construct of social Network in population studies. The Courage-SNI is an instrument useful to identify high risk groups or populations whose social Network is poorer.
Josep Maria Haro - One of the best experts on this subject based on the ideXlab platform.
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the validity of the instrument to evaluate social Network in the ageing population the collaborative research on ageing in europe social Network index
WOS, 2014Co-Authors: Katarzyna Zawisza, Aleksander Galas, Beata Tobiaszadamczyk, Somnath Chatterji, Josep Maria Haro, Marta Miret, Seppo Koskinen, Mick PowerAbstract:The aim of the study was to create a simplified, easy implementable multidimensional instrument to assess all relevant elements of the structure and function of social Network within individuals across different European countries and to provide the tool for health professionals and policy makers. The analysis was based on the sample of 10 446 non-institutionalized adult population from Finland, Poland and Spain. The Social Network Questionnaire Collaborative Research on Ageing in Europe Social Network Index (COURAGE-SNI) was part of the COURAGE questionnaire. The indicators of the functioning of social Network ties (close relations), frequency of direct contact and general support were evaluated. Functions were assess within the main structural components as spouse, parents, children, grandchildren, other relatives, friends, coworkers and neighbours. The exploratory factor analysis revealed five main latent components of social Network with one component composed of hierarchical part. The confirmatory factor analysis provided an acceptable fit for the model. The generalize partial credit model was used to calculate factor scores for five components of the COURAGE-SNI considering the social Networks of ‘spouse/partner’, ‘parents’, ‘other family members’, ‘neighbours’ and ‘friends and co-workers’. The scores for every component were recalculated so as to provide the social Network Saturation ranged from 0 (the lowest) to 100% (the highest possible). Finally, the COURAGE-SNI score was obtained as the sum of weighted information calculated by the item response theory procedure for every aforementioned component. In summary, the COURAGE-SNI showed good reliability and content validity and seems to be a promising tool for the assessment of the social Network phenomenon across European countries. Copyright © 2013 John Wiley & Sons, Ltd. Key Practitioner Message The Courage-SNI is a new tool to assess the construct of social Network in population studies. The Courage-SNI is an instrument useful to identify high risk groups or populations whose social Network is poorer.
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the validity of the instrument to evaluate social Network in the ageing population the collaborative research on ageing in europe social Network index
Clinical Psychology & Psychotherapy, 2014Co-Authors: Katarzyna Zawisza, Aleksander Galas, Beata Tobiaszadamczyk, Somnath Chatterji, Josep Maria Haro, Marta Miret, Seppo Koskinen, Mick PowerAbstract:The aim of the study was to create a simplified, easy implementable multidimensional instrument to assess all relevant elements of the structure and function of social Network within individuals across different European countries and to provide the tool for health professionals and policy makers. The analysis was based on the sample of 10 446 non-institutionalized adult population from Finland, Poland and Spain. The Social Network Questionnaire Collaborative Research on Ageing in Europe Social Network Index (COURAGE-SNI) was part of the COURAGE questionnaire. The indicators of the functioning of social Network ties (close relations), frequency of direct contact and general support were evaluated. Functions were assess within the main structural components as spouse, parents, children, grandchildren, other relatives, friends, coworkers and neighbours. The exploratory factor analysis revealed five main latent components of social Network with one component composed of hierarchical part. The confirmatory factor analysis provided an acceptable fit for the model. The generalize partial credit model was used to calculate factor scores for five components of the COURAGE-SNI considering the social Networks of ‘spouse/partner’, ‘parents’, ‘other family members’, ‘neighbours’ and ‘friends and co-workers’. The scores for every component were recalculated so as to provide the social Network Saturation ranged from 0 (the lowest) to 100% (the highest possible). Finally, the COURAGE-SNI score was obtained as the sum of weighted information calculated by the item response theory procedure for every aforementioned component. In summary, the COURAGE-SNI showed good reliability and content validity and seems to be a promising tool for the assessment of the social Network phenomenon across European countries. Copyright © 2013 John Wiley & Sons, Ltd. Key Practitioner Message The Courage-SNI is a new tool to assess the construct of social Network in population studies. The Courage-SNI is an instrument useful to identify high risk groups or populations whose social Network is poorer.