The Experts below are selected from a list of 12945 Experts worldwide ranked by ideXlab platform
Albert Messmer - One of the best experts on this subject based on the ideXlab platform.
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renaissance a unified Macroscopic Model based approach to real time freeway network traffic surveillance
Transportation Research Part C-emerging Technologies, 2006Co-Authors: Yibing Wang, Markos Papageorgiou, Albert MessmerAbstract:The paper presents a unified Macroscopic Model-based approach to real-time freeway network traffic surveillance as well as a software tool RENAISSANCE that has been recently developed to implement this approach for field applications. RENAISSANCE is designed on the basis of stochastic Macroscopic freeway network traffic flow Modeling, extended Kalman filtering, and a number of traffic surveillance algorithms. Fed with a limited amount of real-time traffic measurements, RENAISSANCE enables a number of freeway network traffic surveillance tasks, including traffic state estimation and short-term traffic state prediction, travel time estimation and prediction, queue tail/head/length estimation and prediction, and incident alarm. The traffic state estimation and prediction lay the operating foundation of RENAISSANCE since RENAISSANCE bases the other traffic surveillance tasks on its traffic state estimation or prediction results. The paper first introduces the utilized stochastic Macroscopic freeway network traffic flow Model and a real-time traffic measurement Model, upon which the complete dynamic system Model of RENAISSANCE is established with special attention to the handling of some important Model parameters. The algorithms for the various traffic surveillance tasks addressed are described along with the functional architecture of the tool. A simulation test was conducted via application of RENAISSANCE to a hypothetical freeway network example with a sparse detector configuration, and the testing results are presented in some detail. Final conclusions and future work are outlined.
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RENAISSANCE - A unified Macroscopic Model-based approach to real-time freeway network traffic surveillance
Transportation Research Part C: Emerging Technologies, 2006Co-Authors: Yin Hai Wang, Markos Papageorgiou, Albert MessmerAbstract:The paper presents a unified Macroscopic Model-based approach to real-time freeway network traffic surveillance as well as a software tool RENAISSANCE that has been recently developed to implement this approach for field applications. RENAISSANCE is designed on the basis of stochastic Macroscopic freeway network traffic flow Modeling, extended Kalman filtering, and a number of traffic surveillance algorithms. Fed with a limited amount of real-time traffic measurements, RENAISSANCE enables a number of freeway network traffic surveillance tasks, including traffic state estimation and short-term traffic state prediction, travel time estimation and prediction, queue tail/head/length estimation and prediction, and incident alarm. The traffic state estimation and prediction lay the operating foundation of RENAISSANCE since RENAISSANCE bases the other traffic surveillance tasks on its traffic state estimation or prediction results. The paper first introduces the utilized stochastic Macroscopic freeway network traffic flow Model and a real-time traffic measurement Model, upon which the complete dynamic system Model of RENAISSANCE is established with special attention to the handling of some important Model parameters. The algorithms for the various traffic surveillance tasks addressed are described along with the functional architecture of the tool. A simulation test was conducted via application of RENAISSANCE to a hypothetical freeway network example with a sparse detector configuration, and the testing results are presented in some detail. Final conclusions and future work are outlined. © 2006 Elsevier Ltd. All rights reserved.
Markos Papageorgiou - One of the best experts on this subject based on the ideXlab platform.
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A new Macroscopic Model for Variable Speed Limits
IFAC-PapersOnLine, 2020Co-Authors: Jose Ramon D. Frejo, Markos Papageorgiou, Ioannis Papamichail, Bart De SchutterAbstract:Abstract This paper proposes a new Macroscopic Model for Variable Speed Limits (VSLs), combining characteristics of previously proposed Models, in order to have the capability of Modeling different capacities, critical densities, and levels of compliance for links affected by speed limits. Moreover, the effects of VSLs on the fundamental diagram of traffic flow are studied concluding that, at least for the considered stretch of the A12 freeway in The Netherlands, the capacity of a freeway link is decreased (and the critical density is increased) by reducing the value of the corresponding speed limit. Finally, it is shown that the VSL-induced fundamental diagram is not triangular and that the speed limit compliance can be very low if enforcement measures are not applied.
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renaissance a unified Macroscopic Model based approach to real time freeway network traffic surveillance
Transportation Research Part C-emerging Technologies, 2006Co-Authors: Yibing Wang, Markos Papageorgiou, Albert MessmerAbstract:The paper presents a unified Macroscopic Model-based approach to real-time freeway network traffic surveillance as well as a software tool RENAISSANCE that has been recently developed to implement this approach for field applications. RENAISSANCE is designed on the basis of stochastic Macroscopic freeway network traffic flow Modeling, extended Kalman filtering, and a number of traffic surveillance algorithms. Fed with a limited amount of real-time traffic measurements, RENAISSANCE enables a number of freeway network traffic surveillance tasks, including traffic state estimation and short-term traffic state prediction, travel time estimation and prediction, queue tail/head/length estimation and prediction, and incident alarm. The traffic state estimation and prediction lay the operating foundation of RENAISSANCE since RENAISSANCE bases the other traffic surveillance tasks on its traffic state estimation or prediction results. The paper first introduces the utilized stochastic Macroscopic freeway network traffic flow Model and a real-time traffic measurement Model, upon which the complete dynamic system Model of RENAISSANCE is established with special attention to the handling of some important Model parameters. The algorithms for the various traffic surveillance tasks addressed are described along with the functional architecture of the tool. A simulation test was conducted via application of RENAISSANCE to a hypothetical freeway network example with a sparse detector configuration, and the testing results are presented in some detail. Final conclusions and future work are outlined.
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RENAISSANCE - A unified Macroscopic Model-based approach to real-time freeway network traffic surveillance
Transportation Research Part C: Emerging Technologies, 2006Co-Authors: Yin Hai Wang, Markos Papageorgiou, Albert MessmerAbstract:The paper presents a unified Macroscopic Model-based approach to real-time freeway network traffic surveillance as well as a software tool RENAISSANCE that has been recently developed to implement this approach for field applications. RENAISSANCE is designed on the basis of stochastic Macroscopic freeway network traffic flow Modeling, extended Kalman filtering, and a number of traffic surveillance algorithms. Fed with a limited amount of real-time traffic measurements, RENAISSANCE enables a number of freeway network traffic surveillance tasks, including traffic state estimation and short-term traffic state prediction, travel time estimation and prediction, queue tail/head/length estimation and prediction, and incident alarm. The traffic state estimation and prediction lay the operating foundation of RENAISSANCE since RENAISSANCE bases the other traffic surveillance tasks on its traffic state estimation or prediction results. The paper first introduces the utilized stochastic Macroscopic freeway network traffic flow Model and a real-time traffic measurement Model, upon which the complete dynamic system Model of RENAISSANCE is established with special attention to the handling of some important Model parameters. The algorithms for the various traffic surveillance tasks addressed are described along with the functional architecture of the tool. A simulation test was conducted via application of RENAISSANCE to a hypothetical freeway network example with a sparse detector configuration, and the testing results are presented in some detail. Final conclusions and future work are outlined. © 2006 Elsevier Ltd. All rights reserved.
Yibing Wang - One of the best experts on this subject based on the ideXlab platform.
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renaissance a unified Macroscopic Model based approach to real time freeway network traffic surveillance
Transportation Research Part C-emerging Technologies, 2006Co-Authors: Yibing Wang, Markos Papageorgiou, Albert MessmerAbstract:The paper presents a unified Macroscopic Model-based approach to real-time freeway network traffic surveillance as well as a software tool RENAISSANCE that has been recently developed to implement this approach for field applications. RENAISSANCE is designed on the basis of stochastic Macroscopic freeway network traffic flow Modeling, extended Kalman filtering, and a number of traffic surveillance algorithms. Fed with a limited amount of real-time traffic measurements, RENAISSANCE enables a number of freeway network traffic surveillance tasks, including traffic state estimation and short-term traffic state prediction, travel time estimation and prediction, queue tail/head/length estimation and prediction, and incident alarm. The traffic state estimation and prediction lay the operating foundation of RENAISSANCE since RENAISSANCE bases the other traffic surveillance tasks on its traffic state estimation or prediction results. The paper first introduces the utilized stochastic Macroscopic freeway network traffic flow Model and a real-time traffic measurement Model, upon which the complete dynamic system Model of RENAISSANCE is established with special attention to the handling of some important Model parameters. The algorithms for the various traffic surveillance tasks addressed are described along with the functional architecture of the tool. A simulation test was conducted via application of RENAISSANCE to a hypothetical freeway network example with a sparse detector configuration, and the testing results are presented in some detail. Final conclusions and future work are outlined.
Yin Hai Wang - One of the best experts on this subject based on the ideXlab platform.
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RENAISSANCE - A unified Macroscopic Model-based approach to real-time freeway network traffic surveillance
Transportation Research Part C: Emerging Technologies, 2006Co-Authors: Yin Hai Wang, Markos Papageorgiou, Albert MessmerAbstract:The paper presents a unified Macroscopic Model-based approach to real-time freeway network traffic surveillance as well as a software tool RENAISSANCE that has been recently developed to implement this approach for field applications. RENAISSANCE is designed on the basis of stochastic Macroscopic freeway network traffic flow Modeling, extended Kalman filtering, and a number of traffic surveillance algorithms. Fed with a limited amount of real-time traffic measurements, RENAISSANCE enables a number of freeway network traffic surveillance tasks, including traffic state estimation and short-term traffic state prediction, travel time estimation and prediction, queue tail/head/length estimation and prediction, and incident alarm. The traffic state estimation and prediction lay the operating foundation of RENAISSANCE since RENAISSANCE bases the other traffic surveillance tasks on its traffic state estimation or prediction results. The paper first introduces the utilized stochastic Macroscopic freeway network traffic flow Model and a real-time traffic measurement Model, upon which the complete dynamic system Model of RENAISSANCE is established with special attention to the handling of some important Model parameters. The algorithms for the various traffic surveillance tasks addressed are described along with the functional architecture of the tool. A simulation test was conducted via application of RENAISSANCE to a hypothetical freeway network example with a sparse detector configuration, and the testing results are presented in some detail. Final conclusions and future work are outlined. © 2006 Elsevier Ltd. All rights reserved.
Mehmet Ali Silgu - One of the best experts on this subject based on the ideXlab platform.
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extension of traffic flow pattern dynamic classification by a Macroscopic Model using multivariate clustering
Transportation Science, 2016Co-Authors: Hilmi Berk Celikoglu, Mehmet Ali SilguAbstract:In this paper, we evaluate the performance of a dynamic approach to classifying flow patterns reconstructed by a switching-mode Macroscopic flow Model considering a multivariate clustering method. To remove noise and tolerate a wide scatter of traffic data, filters are applied before the overall Modeling process. Filtered data are dynamically and simultaneously input to the density estimation and traffic flow Modeling processes. A modified cell transmission Model simulates traffic flow to explicitly account for flow condition transitions considering wave propagations throughout a freeway test stretch. We use flow dynamics specific to each of the cells to determine the mode of prevailing traffic conditions. Flow dynamics are then reconstructed by neural methods. By using two methods in part, i.e., dynamic classification and nonhierarchical clustering, classification of flow patterns over the fundamental diagram is obtained by considering traffic density as a pattern indicator. The fundamental diagram of speed-flow is dynamically updated to specify the current corresponding flow pattern. The dynamic classification approach returned promising results in capturing sudden changes on test stretch flow patterns as well as performance compared to multivariate clustering. The dynamic methods applied here are open to use in practice within intelligent management strategies, including incident detection and control and variable speed management.
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Extension of Traffic Flow Pattern Dynamic Classification by a Macroscopic Model Using Multivariate Clustering
Transportation Science, 2016Co-Authors: Hilmi Berk Celikoglu, Mehmet Ali SilguAbstract:In this paper, we evaluate the performance of a dynamic approach to classifying flow patterns reconstructed by a switching-mode Macroscopic flow Model considering a multivariate clustering method. To remove noise and tolerate a wide scatter of traffic data, filters are applied before the overall Modeling process. Filtered data are dynamically and simultaneously input to the density estimation and traffic flow Modeling processes. A modified cell transmission Model simulates traffic flow to explicitly account for flow condition transitions considering wave propagations throughout a freeway test stretch. We use flow dynamics specific to each of the cells to determine the mode of prevailing traffic conditions. Flow dynamics are then reconstructed by neural methods. By using two methods in part, i.e., dynamic classification and nonhierarchical clustering, classification of flow patterns over the fundamental diagram is obtained by considering traffic density as a pattern indicator. The fundamental diagram of sp...
