The Experts below are selected from a list of 21153 Experts worldwide ranked by ideXlab platform
Aya Aboudina - One of the best experts on this subject based on the ideXlab platform.
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a bi level distributed approach for optimizing time dependent congestion pricing in large networks a simulation based case study in the greater toronto area
Transportation Research Part C-emerging Technologies, 2017Co-Authors: Aya Aboudina, Baher AbdulhaiAbstract:Abstract Congestion pricing is one of the most widely contemplated methods to Manage Traffic congestion by charging fees for the use of roads, more where and when it is congested, and less where and when it is not. This study presents a bi-level distributed approach for optimal time-dependent congestion pricing in large networks. The bi-level procedure involves a theoretical model of dynamic congestion pricing and a distributed optimization algorithm. The bi-level toll optimization module is integrated into a testbed of hybrid departure time choice and dynamic Traffic assignment simulation models for the Greater Toronto Area (GTA). The integrated system provides a unified (location- and time-specific) congestion pricing system that determines optimal tolling and evaluates its impact on road Traffic congestion and travellers’ behavioural choices, including departure time and route choices. For the system’s large-scale nature and the consequent computational challenges, the optimization algorithm is executed concurrently on a parallel computing cluster. The system is applied to a simulation-based case study of tolling major highways in the GTA while capturing the network-wide regional effects of tolling. The travel demand and drivers’ attributes are extracted from regional household travel survey data that reflect travellers’ heterogeneity. The main results indicate that: (1) optimal variable pricing reflects congestion patterns and induces departure time re-scheduling and rerouting patterns, resulting in improved average travel times and schedule delays, (2) optimal tolls intended to Manage Traffic demand are significantly lower than those intended to maximize toll revenues, (3) tolled routes have different sensitivities to identical toll changes, (4) the start times of longer trips are more sensitive (elastic) to variable distance-based tolling policies compared to shorter trips, (5) toll payers benefit from tolling even before toll revenues are spent, and (6) the optimal tolling policies determined offer a win–win solution in which travel times are improved while also raising funds to invest in sustainable transportation infrastructure.
Baher Abdulhai - One of the best experts on this subject based on the ideXlab platform.
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a bi level distributed approach for optimizing time dependent congestion pricing in large networks a simulation based case study in the greater toronto area
Transportation Research Part C-emerging Technologies, 2017Co-Authors: Aya Aboudina, Baher AbdulhaiAbstract:Abstract Congestion pricing is one of the most widely contemplated methods to Manage Traffic congestion by charging fees for the use of roads, more where and when it is congested, and less where and when it is not. This study presents a bi-level distributed approach for optimal time-dependent congestion pricing in large networks. The bi-level procedure involves a theoretical model of dynamic congestion pricing and a distributed optimization algorithm. The bi-level toll optimization module is integrated into a testbed of hybrid departure time choice and dynamic Traffic assignment simulation models for the Greater Toronto Area (GTA). The integrated system provides a unified (location- and time-specific) congestion pricing system that determines optimal tolling and evaluates its impact on road Traffic congestion and travellers’ behavioural choices, including departure time and route choices. For the system’s large-scale nature and the consequent computational challenges, the optimization algorithm is executed concurrently on a parallel computing cluster. The system is applied to a simulation-based case study of tolling major highways in the GTA while capturing the network-wide regional effects of tolling. The travel demand and drivers’ attributes are extracted from regional household travel survey data that reflect travellers’ heterogeneity. The main results indicate that: (1) optimal variable pricing reflects congestion patterns and induces departure time re-scheduling and rerouting patterns, resulting in improved average travel times and schedule delays, (2) optimal tolls intended to Manage Traffic demand are significantly lower than those intended to maximize toll revenues, (3) tolled routes have different sensitivities to identical toll changes, (4) the start times of longer trips are more sensitive (elastic) to variable distance-based tolling policies compared to shorter trips, (5) toll payers benefit from tolling even before toll revenues are spent, and (6) the optimal tolling policies determined offer a win–win solution in which travel times are improved while also raising funds to invest in sustainable transportation infrastructure.
Lu Liu - One of the best experts on this subject based on the ideXlab platform.
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An Efficient SDN Load Balancing Scheme Based on Variance Analysis for Massive Mobile Users
Hindawi Limited, 2015Co-Authors: Hong Zhong, Qunfeng Lin, Jie Cui, Runhua Shi, Lu LiuAbstract:In a traditional network, server load balancing is used to satisfy the demand for high data volumes. The technique requires large capital investment while offering poor scalability and flexibility, which difficultly supports highly dynamic workload demands from massive mobile users. To solve these problems, this paper analyses the principle of software-defined networking (SDN) and presents a new probabilistic method of load balancing based on variance analysis. The method can be used to dynamically Manage Traffic flows for supporting massive mobile users in SDN networks. The paper proposes a solution using the OpenFlow virtual switching technology instead of the traditional hardware switching technology. A SDN controller monitors data Traffic of each port by means of variance analysis and provides a probability-based selection algorithm to redirect Traffic dynamically with the OpenFlow technology. Compared with the existing load balancing methods which were designed to support traditional networks, this solution has lower cost, higher reliability, and greater scalability which satisfy the needs of mobile users
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An Efficient SDN load balancing scheme based on variance analysis for massive mobile users
'Hindawi Limited', 2015Co-Authors: Zhong Hong, Lin Qunfeng, Cui Jie, Shi Runhua, Lu LiuAbstract:In a traditional network, server load balancing is used to satisfy the demand for high data volumes. The technique requires large capital investment while offering poor scalability and flexibility, which difficultly supports highly dynamic workload demands from massive mobile users. To solve these problems, this paper analyses the principle of software-defined networking (SDN) and presents a new probabilistic method of load balancing based on variance analysis. The method can be used to dynamically Manage Traffic flows for supporting massive mobile users in SDN networks. The paper proposes a solution using the OpenFlow virtual switching technology instead of the traditional hardware switching technology. A SDN controller monitors data Traffic of each port by means of variance analysis and provides a probability-based selection algorithm to redirect Traffic dynamically with the OpenFlow technology. Compared with the existing load balancing methods which were designed to support traditional networks, this solution has lower cost, higher reliability, and greater scalability which satisfy the needs of mobile users.The work was supported by the National Natural Science Foundation of China (no. 61173188, no. 61572001, and no. 61502008), the Research Fund for the Doctoral Program of Higher Education (no. 20133401110004), the Educational Commission of Anhui Province, China (no. KJ2013A017), the Natural Science Foundation of Anhui Province (no. 1508085QF132), the Tender Project of the Co-Innovation Center for Information Supply & Assurance Technology of Anhui University (no. ADXXBZ2014-7), and the Doctoral Research Startup Funds Project of Anhui University
Christopher M Day - One of the best experts on this subject based on the ideXlab platform.
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integration of real time pedestrian performance measures into existing infrastructure of Traffic signal system
Transportation Research Record, 2008Co-Authors: Sarah M Hubbard, Darcy M Bullock, Christopher M DayAbstract:Transportation system Management requires balancing the needs of many users and multiple transportation modes. Historically, Traffic engineers have relied on short-term engineering studies and intuition to Manage Traffic signal systems. The broad consensus in the Traffic engineering community is that real-time performance measures would enable better operations. Motivation and means are presented to provide real-time pedestrian performance measures using existing controller and vehicle detection technology. Applicable pedestrian service models are identified, and procedures for collecting data for pedestrian performance measures are recommended. The resulting pedestrian performance measures can be presented in an easy-to-interpret visual format that provides a valuable tool for assessing and comparing pedestrian service. Pedestrian service may be compared at different crosswalks in the jurisdiction for prioritization purposes, or at the same crosswalk under different conditions. The proposed pedestrian performance measures may be used in conjunction with existing vehicle performance measures, resulting in an integrated approach to assessing the level of service for vehicles and pedestrians under different conditions and for different signal timing plans.
Matthew Barth - One of the best experts on this subject based on the ideXlab platform.
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real time video based Traffic measurement and visualization system for energy emissions
IEEE Transactions on Intelligent Transportation Systems, 2012Co-Authors: Brendan Morris, Cuong Tran, George Scora, Mohan M Trivedi, Matthew BarthAbstract:The ability to monitor the state of a given roadway in order to better Manage Traffic congestion has become increasingly important. Sophisticated Traffic Management systems able to process both the static and mobile sensor data and provide Traffic information for the roadway network are under development. In addition to typical Traffic data such as flow, density, and average Traffic speed, there is now strong interest in environmental factors such as greenhouse gases, pollutant emissions, and fuel consumption. It is now possible to combine high-resolution real-time Traffic data with instantaneous emission models to estimate these environmental measures in real time. In this paper, a system that estimates average Traffic fuel economy, CO2 , CO, HC, and NOx emissions using a computer-vision-based methodology in combination with vehicle-specific power-based energy and emission models is presented. The CalSentry system provides not only typical Traffic measures but also gives individual vehicle trajectories (instantaneous dynamics) and recognizes vehicle categories, which are used in the emission models to predict environmental parameters. This estimation process provides far more dynamic and accurate environmental information compared with static emission inventory estimation models.
