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An integrated and optimal scheduling of a public transport system in metro Manila using genetic algorithm

机译:基于遗传算法的马尼拉大都会公交系统综合优化调度

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Selection of dispatching modes for a transit system is a very important aspect of the schedule problem. This paper aims to optimize and monitor the scheduling and dispatching of public utility vehicles (PUV) plying along EDSA. Using passenger and vehicle data, the system will analyze an optimal scheduling pattern for dispatching PUVs in terminals that covers EDSA routes. The scheduling is based on passenger demand and congestion along the route. The scheduling system will be based on the dispatch system used by the Bus Rapid Transit. There are three modes of dispatch scheduling: normal scheduling, zone scheduling and express scheduling. It seeks to optimize the dispatch system in such a way that the transfer time of passengers at the transfer nodes is minimized while the operational constraints such as the traffic demand, departure time and maximum (minimum) headway are satisfied. Mathematical model illustrates the dynamics and behaviour of the system under different constraints. Genetic algorithm is used as the optimization tool. Data necessary for the generation of the algorithm came from transportation surveys. The code was written using C++ program. Effectiveness, accuracy and robustness of the system are evident by the results.
机译:选择运输系统的调度方式是调度问题的一个非常重要的方面。本文旨在优化和监控沿EDSA行驶的公共事业车辆(PUV)的调度和调度。使用乘客和车辆数据,系统将分析用于在覆盖EDSA路线的终端中调度PUV的最佳调度模式。调度基于路线上的乘客需求和拥堵情况。调度系统将基于快速公交使用的调度系统。调度调度有三种模式:普通调度,区域调度和快速调度。它试图以这样一种方式来优化调度系统,即在满足交通需求,出发时间和最大(最小)行驶距离等运营约束的同时,将乘客在换乘节点的换乘时间最小化。数学模型说明了在不同约束条件下系统的动力学和行为。遗传算法被用作优化工具。生成算法所需的数据来自运输调查。该代码是使用C ++程序编写的。结果证明了该系统的有效性,准确性和鲁棒性。

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