دانلود مقاله ISI انگلیسی شماره 5827
عنوان فارسی مقاله

طراحی شبکه حمل و نقل توسط بهینه سازی کلونی زنبور عسل

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
5827 2013 11 صفحه PDF سفارش دهید 10560 کلمه
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Transit network design by Bee Colony Optimization
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Expert Systems with Applications, Available online 10 May 2013

کلمات کلیدی
- ( طراحی شبکه حمل و نقل - بهینه سازی کلونی زنبور عسل () - ازدحام اطلاعات
پیش نمایش مقاله
پیش نمایش مقاله طراحی شبکه حمل و نقل توسط بهینه سازی کلونی زنبور عسل

چکیده انگلیسی

The transit network design problem is one of the most significant problems faced by transit operators and city authorities in the world. This transportation planning problem belongs to the class of difficult combinatorial optimization problem, whose optimal solution is difficult to discover. The paper develops a Swarm Intelligence (SI) based model for the transit network design problem. When designing the transit network, we try to maximize the number of satisfied passengers, to minimize the total number of transfers, and to minimize the total travel time of all served passengers. Our approach to the transit network design problem is based on the Bee Colony Optimization (BCO) metaheuristics. The BCO algorithm is a stochastic, random-search technique that belongs to the class of population-based algorithms. This technique uses a similarity among the way in which bees in nature look for food, and the way in which optimization algorithms search for an optimum of a combinatorial optimization problem. The numerical experiments are performed on known benchmark problems. We clearly show that our approach, based on the BCO algorithm, is competitive with other approaches in the literature, and it can generate high-quality solutions.

مقدمه انگلیسی

Urban road networks in a lot of countries are extremely congested. The consequences are high travel times, unforeseen delays, increased travel costs, increased air pollution, noise level, and number of traffic accidents. Transportation engineers and city authorities have developed and implemented various Travel Demand Management (TDM) techniques that increase travel choices to travelers (“Park-and-Ride facilities”, “High Occupancy Vehicle (HOV) facilities”, “Ride-sharing programs”, “Telecommuting”, “Congestion Pricing”). Still, the raising of the modal share of public transit in the cities is one of the major activities to be performed by traffic planners and city authorities. This could be done by proper design of public transit networks, expansion of existing lines and park and ride spaces, increasing the availability of direct service among origin–destination pairs, frequencies increase, development of the bus systems separated from the rest of the traffic network, transit service on nights and weekends, improving passengers’ comfort and schedule reliability, better information systems for passengers (visual terminals and broadcasting information), etc. Properly designed public transit network can significantly increase public transport mode share. The public transit network design problem is one of the most significant problems faced by bus operators and city authorities in the world. This transportation planning problem belongs to the class of difficult combinatorial optimization problem, whose optimal solution is difficult to discover. The bus network shape, as well as bus frequencies, highly depend on both passenger demand, and on the number and type of available buses (fleet size), and/or available budget. Poorly designed bus network can cause very long passengers’ waiting times, and/or inexactness in bus arriving times. In addition, inadequately designed network can show high inappropriateness among the designed bus routes and paths of the majority of users. Many of the factors that should be taken into account when designing bus network are mutually in conflict. For example, the shorter passengers waiting times, the higher the number of buses needed, etc. When designing the bus network, the interests of both the operator and the passenger must be taken into account. Due to the conflicting nature of these interests, we treat the bus network design problem as a multicriteria decision-making problem. When designing the transit network, we try to maximize the number of satisfied passengers, to minimize the total number of transfers, and to minimize the total travel time of all served passengers. In this paper we develop the model for the bus network design problem. Our approach is based on the Bee Colony Optimization (BCO) metaheuristics. The BCO algorithm is a stochastic, random-search technique that belongs to the class of population-based algorithms. This technique uses a similarity among the way in which bees in nature look for food, and the way in which optimization algorithms search for an optimum of a combinatorial optimization problem. The numerical experiments are performed on known benchmark problems, as well as on the problems generated by the authors of the paper. Our approach is competitive with other approaches in the literature, and it can generate high-quality solutions within negligible CPU times. The paper is organized in the following way. Literature review is given in Section 2. Section 3 contains statement of the problem. Proposed solution to the problem is given in Section 4. The BCO approach to the transit network design problem is explained in details in Section 5. Experimental evaluation of the proposed approach is given in Section 6. Recommendations for future research and conclusion are given in Section 7.

نتیجه گیری انگلیسی

We developed the model for the transit network design problem. The transit network design problem is a large combinatorial problem whose optimal solution is difficult to find, therefore a heuristic approach must be used. The model proposed in this paper is based on the Swarm Intelligence concepts. We tried to maximize the number of served passengers, to minimize the total in-vehicle time of all served passengers, and to minimize the total number of transfers in the network. We clearly showed that the proposed BCO algorithm is competitive with other approaches in the literature, and that it can generate high-quality solutions within reasonable CPU times. The challenge for the future research is to test the offered BCO concept simultaneously with the assumption that passenger flows depend on the transit network design, as well as in the case of the multiple path passengers’ assignment among possible transit routes. The proposed concept should be expanded in the future research by including in the analysis available number of vehicles, as well as some other operational constraints.

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