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- 2019
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Tidskriftsartikel (refereegranskat)
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| 2. |
- Bagloee, S. A., et al.
(författare)
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A Mixed User-Equilibrium and System-Optimal Traffic Flow for Connected Vehicles Stated as a Complementarity Problem
- 2017
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Ingår i: Computer-Aided Civil and Infrastructure Engineering. - : Wiley. - 1093-9687 .- 1467-8667. ; 32:7, s. 562-580
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Tidskriftsartikel (refereegranskat)abstract
- Connected vehicles (CVs), be they autonomous vehicles or a fleet of cargo carriers or Uber, are a matter of when they become a reality and not if. It is not unreasonable to think that CV technology may have a far-reaching impact, even to the genesis of a completely new traffic pattern. To this end, the literature has yet to address the routing behavior of the CVs, namely traffic assignment problem (TAP) (perhaps it is assumed, they ought to follow the traditional shortest possible paths, known as user equilibrium [UE]). It is possible that real-time data could be derived from the vehicles' communications that in turn could be used to achieve a better traffic circulation. In this article, we propose a mathematical formulation to ensure the CVs are seeking the system optimal (SO) principles, while the remainder continue to pursue the old-fashioned UE pattern. The model is formulated as a nonlinear complementarity problem (NCP). This article contributes to the literature in three distinct ways: (i) mathematical formulation for the CVs' routing, stated as a mixed UE-SO traffic pattern, is proposed; (ii) a variety of realistic features are explicitly considered in the solution to the TAP including road capacity, elastic demand, multiclass and asymmetric travel time; and (iii) formal proof of the existence and uniqueness of the solutions are also presented. The proposed methodology is applied to the networks of Sioux-Falls and Melbourne.
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| 3. |
- Bagloee, S. A., et al.
(författare)
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A hybrid machine-learning and optimization method for contraflow design in post-disaster cases and traffic management scenarios
- 2019
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Ingår i: Expert systems with applications. - : Elsevier. - 0957-4174 .- 1873-6793. ; 124, s. 67-81
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Tidskriftsartikel (refereegranskat)abstract
- The growing number of man-made and natural disasters in recent years has made the disaster management a focal point of interest and research. To assist and streamline emergency evacuation, changing the directions of the roads (called contraflow, a traffic control measure) is proven to be an effective, quick and affordable scheme in the action list of the disaster management. The contraflow is computationally a challenging problem (known as NP-hard), hence developing an efficient method applicable to real-world and large-sized cases is a significant challenge in the literature. To cope with its complexities and to tailor to practical applications, a hybrid heuristic method based on a machine-learning model and bilevel optimization is developed. The idea is to try and test several contraflow scenarios providing a training dataset for a supervised learning (regression) model which is then used in an optimization framework to find a better scenario in an iterative process. This method is coded as a single computer program synchronized with GAMS (for optimization), MATLAB (for machine learning), EMME3 (for traffic simulation), MS-Access (for data storage) and MS-Excel (as an interface), and it is tested using a real dataset from Winnipeg, and Sioux-Falls as benchmarks. The algorithm managed to find globally optimal solutions for the Sioux-Falls example and improved accessibility to the dense and congested central areas of Winnipeg just by changing the direction of some roads.
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| 4. |
- Bagloee, S. A., et al.
(författare)
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Minimization of water pumps' electricity usage: A hybrid approach of regression models with optimization
- 2018
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Ingår i: Expert Systems with Applications. - : Elsevier BV. - 0957-4174. ; 107, s. 222-242
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Tidskriftsartikel (refereegranskat)abstract
- Due to pervasive deployment of electricity-propelled water-pumps, water distribution systems (WDSs) are energy-intensive technologies which are largely operated and controlled by engineers based on their judgments and discretions. Hence energy efficiency in the water sector is a serious concern. To this end, this study is dedicated to the optimal operation of the WDS which is articulated as minimization of the pumps' energy consumption while maintaining flow, pressure, and tank water levels at a minimum level, also known as pump scheduling problem (PSP). This problem is proved to be NP-hard (i.e. a difficult problem computationally). We therefore develop a hybrid methodology incorporating machine-learning techniques as well as optimization methods to address real-life and large-sized WDSs. Other main contributions of this research are (i) in addition to fixed-speed pumps, the variable-speed pumps are optimally controlled, (ii) and operational rules such as water allocation rules can also be explicitly considered in the methodology. This methodology is tested using a large dataset in which the results are found to be highly promising. This methodology has been coded as a user-friendly software composed of MS-Excel (as a user interface), MS-Access (a database), MATLAB (for machine-learning), GAMS (with CPLEX solver for solving optimization problem) and EPANET (to solve hydraulic models). (C) 2018 Elsevier Ltd. All rights reserved.
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| 5. |
- Bagloee, S.A., et al.
(författare)
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Minimization of water pumps' electricity usage: a machine-learning approach
- 2017
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Due to pervasive deployment of electricity-propelled water-pumps, water distribution systems (WDSs) are energy-intensive technologies which are largely operated and controlled by engineers based on their judgments and discretions. Hence energy efficiency in the water sector is a serious concern. To this end, this study is dedicated to the optimal operation of the WDS which is articulated as minimization of the pumps’ energy consumption while maintaining flow, pressure, and tank water levels at a minimum level, also known as pump scheduling problem (PSP). This problem is proved to be of the most difficult problem (namely NP-hard). To this end, we develop a hybrid methodology consists of machine learning techniques as well as optimization methods, that is to address real life and large sized WDSs. Other main contributions of this research are (i) also, variable speed pumps can be modeled and optimally controlled, (ii) operational rules such as water allocation rules can also be explicitly considered in the methodology. This methodology is tested using a large sized dataset in which the results are found to be highly promising. This methodology has been coded as a user-friendly software composed of MS-Excel (as a user interface), MS-Access (a database), MATLAB (for machine learning), GAMS (with CPLEX solver for solving optimization problem) and EPANET (to solve hydraulic models).
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