The Experts below are selected from a list of 11064 Experts worldwide ranked by ideXlab platform

Khaled Abdelghany - One of the best experts on this subject based on the ideXlab platform.

  • Real-time traffic Network State estimation and prediction with decision support capabilities: Application to integrated corridor management
    Transportation Research Part C: Emerging Technologies, 2016
    Co-Authors: Hossein Hashemi, Khaled Abdelghany
    Abstract:

    Abstract This paper presents a real-time traffic Network State estimation and prediction system with built-in decision support capabilities for traffic Network management. The system provides traffic Network managers with the capabilities to estimate the current Network conditions, predict congestion dynamics, and generate efficient traffic management schemes for recurrent and non-recurrent congestion situations. The system adopts a closed-loop rolling horizon framework in which Network State estimation and prediction modules are integrated with a traffic Network manager module to generate efficient proactive traffic management schemes. The traffic Network manger adopts a meta-heuristic search mechanism to construct the schemes by integrating a wide variety of control strategies. The system is applied in the context of Integrated Corridor Management (ICM), which is envisioned to provide a system approach for managing congested urban corridors. A simulation-based case study is presented for the US-75 corridor in Dallas, Texas. The results show the ability of the system to improve the overall Network performance during hypothetical incident scenarios.

  • Real-Time Traffic Network State Prediction for Proactive Traffic Management: Simulation Experiments and Sensitivity Analysis
    Transportation Research Record, 2015
    Co-Authors: Hossein Hashemi, Khaled Abdelghany
    Abstract:

    Real-time traffic management systems with integrated proactive decision support capabilities are expected to operate with (a) limited prediction accuracy (b) decision-making latency, and (c) partial coverage of the managed area. Such deficiencies are difficult to avoid in most real-world traffic Network management applications, and there is a need to quantify the effect of these deficiencies on the performance of traffic Network management systems. This paper studies the effectiveness of a proactive traffic management system. Various levels of prediction accuracy of the traffic Network State, decision-making latency, and partial area coverage are considered. A traffic management system that emulates real-time operations is developed. The system adopts a closed-loop rolling horizon framework, which integrates Network State estimation and prediction modules as well as decision support capabilities. A set of simulation experiments considers a hypothetical highway Network. The results show that the effectiven...

  • Rollback Approach for Demand Consistency Checking of Real-Time Traffic Network State Estimation Models
    Transportation Research Record, 2014
    Co-Authors: Ala Alnawaiseh, Khaled Abdelghany, Ahmed Hassan
    Abstract:

    The paper presents a real-time traffic Network State estimation model with online demand consistency checking and updating capabilities. In contrast to reactive-based methodologies proposed in the literature, the model adopted a time rollback with a corrective actions approach. When an instance of inconsistency between the measured and estimated Network State was observed, the model was allowed to roll back in time and promptly resimulated a predefined past period after the appropriate model's parameters were adjusted to minimize the observed inconsistency. A demand correction algorithm was developed and used for demand adjustment for each rollback period. The results of applying the developed model for a test bed Network are presented. Results show that the approach improves the model's consistency with real-world observations.

  • Real-Time Traffic Network State Estimation and Prediction withDecision Support Capabilities: Application to Integrated Corridor Management
    2013
    Co-Authors: Hossein Hashemi, Khaled Abdelghany, Ahmed Hassan, M Maverick Lezar
    Abstract:

    This paper presents a real-time traffic Network State estimation and prediction system with built-in decision support capabilities for traffic Network management. The system seeks to provide traffic Network managers with the capabilities to estimate the current Network conditions, predict congestion dynamics, and generate efficient traffic management schemes for recurrent and non-recurrent congestion situations. The system adopts a closed-loop rolling horizon framework in which Network State estimation and prediction modules are integrated. The system is applied in the context of Integrated Corridor Management (ICM), which is envisioned to provide a system-based approach for managing congested urban corridors. A genetic algorithm methodology is developed to generate efficient traffic management schemes that integrate preapproved control actions by all managing agencies. The system is applied to a section of a commuter corridor in Dallas, Texas. The results show the ability of the system to improve the overall Network performance during hypothetical incident scenarios.

Hossein Hashemi - One of the best experts on this subject based on the ideXlab platform.

  • Real-time traffic Network State estimation and prediction with decision support capabilities: Application to integrated corridor management
    Transportation Research Part C: Emerging Technologies, 2016
    Co-Authors: Hossein Hashemi, Khaled Abdelghany
    Abstract:

    Abstract This paper presents a real-time traffic Network State estimation and prediction system with built-in decision support capabilities for traffic Network management. The system provides traffic Network managers with the capabilities to estimate the current Network conditions, predict congestion dynamics, and generate efficient traffic management schemes for recurrent and non-recurrent congestion situations. The system adopts a closed-loop rolling horizon framework in which Network State estimation and prediction modules are integrated with a traffic Network manager module to generate efficient proactive traffic management schemes. The traffic Network manger adopts a meta-heuristic search mechanism to construct the schemes by integrating a wide variety of control strategies. The system is applied in the context of Integrated Corridor Management (ICM), which is envisioned to provide a system approach for managing congested urban corridors. A simulation-based case study is presented for the US-75 corridor in Dallas, Texas. The results show the ability of the system to improve the overall Network performance during hypothetical incident scenarios.

  • Real-Time Traffic Network State Prediction for Proactive Traffic Management: Simulation Experiments and Sensitivity Analysis
    Transportation Research Record, 2015
    Co-Authors: Hossein Hashemi, Khaled Abdelghany
    Abstract:

    Real-time traffic management systems with integrated proactive decision support capabilities are expected to operate with (a) limited prediction accuracy (b) decision-making latency, and (c) partial coverage of the managed area. Such deficiencies are difficult to avoid in most real-world traffic Network management applications, and there is a need to quantify the effect of these deficiencies on the performance of traffic Network management systems. This paper studies the effectiveness of a proactive traffic management system. Various levels of prediction accuracy of the traffic Network State, decision-making latency, and partial area coverage are considered. A traffic management system that emulates real-time operations is developed. The system adopts a closed-loop rolling horizon framework, which integrates Network State estimation and prediction modules as well as decision support capabilities. A set of simulation experiments considers a hypothetical highway Network. The results show that the effectiven...

  • Real-Time Traffic Network State Estimation and Prediction withDecision Support Capabilities: Application to Integrated Corridor Management
    2013
    Co-Authors: Hossein Hashemi, Khaled Abdelghany, Ahmed Hassan, M Maverick Lezar
    Abstract:

    This paper presents a real-time traffic Network State estimation and prediction system with built-in decision support capabilities for traffic Network management. The system seeks to provide traffic Network managers with the capabilities to estimate the current Network conditions, predict congestion dynamics, and generate efficient traffic management schemes for recurrent and non-recurrent congestion situations. The system adopts a closed-loop rolling horizon framework in which Network State estimation and prediction modules are integrated. The system is applied in the context of Integrated Corridor Management (ICM), which is envisioned to provide a system-based approach for managing congested urban corridors. A genetic algorithm methodology is developed to generate efficient traffic management schemes that integrate preapproved control actions by all managing agencies. The system is applied to a section of a commuter corridor in Dallas, Texas. The results show the ability of the system to improve the overall Network performance during hypothetical incident scenarios.

Ahmed Hassan - One of the best experts on this subject based on the ideXlab platform.

  • Rollback Approach for Demand Consistency Checking of Real-Time Traffic Network State Estimation Models
    Transportation Research Record, 2014
    Co-Authors: Ala Alnawaiseh, Khaled Abdelghany, Ahmed Hassan
    Abstract:

    The paper presents a real-time traffic Network State estimation model with online demand consistency checking and updating capabilities. In contrast to reactive-based methodologies proposed in the literature, the model adopted a time rollback with a corrective actions approach. When an instance of inconsistency between the measured and estimated Network State was observed, the model was allowed to roll back in time and promptly resimulated a predefined past period after the appropriate model's parameters were adjusted to minimize the observed inconsistency. A demand correction algorithm was developed and used for demand adjustment for each rollback period. The results of applying the developed model for a test bed Network are presented. Results show that the approach improves the model's consistency with real-world observations.

  • Real-Time Traffic Network State Estimation and Prediction withDecision Support Capabilities: Application to Integrated Corridor Management
    2013
    Co-Authors: Hossein Hashemi, Khaled Abdelghany, Ahmed Hassan, M Maverick Lezar
    Abstract:

    This paper presents a real-time traffic Network State estimation and prediction system with built-in decision support capabilities for traffic Network management. The system seeks to provide traffic Network managers with the capabilities to estimate the current Network conditions, predict congestion dynamics, and generate efficient traffic management schemes for recurrent and non-recurrent congestion situations. The system adopts a closed-loop rolling horizon framework in which Network State estimation and prediction modules are integrated. The system is applied in the context of Integrated Corridor Management (ICM), which is envisioned to provide a system-based approach for managing congested urban corridors. A genetic algorithm methodology is developed to generate efficient traffic management schemes that integrate preapproved control actions by all managing agencies. The system is applied to a section of a commuter corridor in Dallas, Texas. The results show the ability of the system to improve the overall Network performance during hypothetical incident scenarios.

Xu Jianmin - One of the best experts on this subject based on the ideXlab platform.

  • Research on Urban Traffic Network State Evaluation Based on a Traffic Network State Coefficient
    Journal of Highway and Transportation Research and Development, 2011
    Co-Authors: Xu Jianmin
    Abstract:

    To cope with the problem that intersection was put into road sections more from the meso perspective while ignoring the effect of intersection to traffic Network State in previous studies of urban traffic Network State evaluation,the basic parameters of traffic Network State evaluation such as intersection entrance saturation and mean space speed of road sections were chosen,combining the macro road Network State evaluation and microscopic traffic State analysis,a new traffic Network State coefficient was defined as the traffic flow moving resistance.The critical value of the traffic Network coefficient was studied and determined.A new method of road traffic Network State analysis was put forward and the whole simulated road Network State was analyed.Finally,the whole road Network State was evaluated and analyed from the aspects of accessibility and connectivity.

Zhu Han - One of the best experts on this subject based on the ideXlab platform.

  • On the impact of Network-State knowledge on the Feasibility of secrecy
    2013
    Co-Authors: Samir M. Perlaza, Arsenia Chorti, H. Vincent Poor, Zhu Han
    Abstract:

    In this paper, the impact of Network-State knowledge is studied in the context of decentralized active non-colluding eavesdropping. The main contribution is a formal proof of a paradoxical effect that might appear when increasing the available knowledge at each of the Network components. Using a broadcast channel similar to the time-division downlink of a single-cell cellular system, it is shown that providing more knowledge to both the transmitter and the receivers negatively affects their performance. Eavesdroppers become more conservative in their attacks, which makes them harmless in terms of information leakage, whereas the transmitter becomes more careful and less willing to transmit, which reduces the expected secrecy capacity of this channel. Finally, it is shown that this counter-intuitive effect vanishes in the high SNR regime, in which the system becomes resilient to active attacks.

  • On the Tradeoffs Between Network State Knowledge and Secrecy
    2013
    Co-Authors: Samir M. Perlaza, Arsenia Chorti, H. Vincent Poor, Zhu Han
    Abstract:

    In this paper, the impact of Network-State knowledge on the feasibility of secrecy is studied in the context of non-colluding active eavesdropping. The main contribution is the investigation of several scenarios in which increasing the available knowledge at each of the Network components leads to some paradoxical observations in terms of the average secrecy capacity and average information leakage. These observations are in the context of a broadcast channel similar to the time-division downlink of a single-cell cellular system. Here, providing more knowledge to the eavesdroppers makes them more conservative in their attacks, and thus, less harmful in terms of average information leakage. Similarly, providing more knowledge to the transmitter makes it more careful and less willing to transmit, which reduces the expected secrecy capacity. These findings are illustrated with a numerical analysis that shows the impact of most of the Network parameters in the feasibility of secrecy.

  • WPMC - On the tradeoffs between Network State knowledge and secrecy
    2013
    Co-Authors: Samir M. Perlaza, Arsenia Chorti, H. Vincent Poor, Zhu Han
    Abstract:

    In this paper, the impact of Network-State knowledge on the feasibility of secrecy is studied in the context of non-colluding active eavesdropping. The main contribution is the investigation of several scenarios in which increasing the available knowledge at each of the Network components leads to some paradoxical observations in terms of the average secrecy capacity and average information leakage. These observations are in the context of a broadcast channel similar to the time-division downlink of a single-cell cellular system. Here, providing more knowledge to the eavesdroppers makes them more conservative in their attacks, and thus, less harmful in terms of average information leakage. Similarly, providing more knowledge to the transmitter makes it more careful and less willing to transmit, which reduces the expected secrecy capacity. These findings are illustrated with a numerical analysis that shows the impact of most of the Network parameters in the feasibility of secrecy.

  • ISIT - On the impact of Network-State knowledge on the Feasibility of secrecy
    2013 IEEE International Symposium on Information Theory, 2013
    Co-Authors: Samir M. Perlaza, Arsenia Chorti, H. Vincent Poor, Zhu Han
    Abstract:

    In this paper, the impact of Network-State knowledge is studied in the context of decentralized active non-colluding eavesdropping. The main contribution is a formal proof of a paradoxical effect that might appear when increasing the available knowledge at each of the Network components. Using a broadcast channel similar to the time-division downlink of a single-cell cellular system, it is shown that providing more knowledge to both the transmitter and the receivers negatively affects their performance. Eavesdroppers become more conservative in their attacks, which makes them harmless in terms of information leakage, whereas the transmitter becomes more careful and less willing to transmit, which reduces the expected secrecy capacity of this channel. Finally, it is shown that this counter-intuitive effect vanishes in the high SNR regime, in which the system becomes resilient to active attacks.