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A cost-minimization model for bus fleet allocation featuring the tactical generation of short-turning and interlining options

机译:一种用于战术的短途转乘和中间方案生成的公交车队分配成本最小化模型

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摘要

Urban public transport operations in peak periods are characterized by highly uneven demand distributions and scarcity of resources. In this work, we propose a rule-based method for systematically generating and integrating alternative lining options, such as short-turning and interlining lines, into the frequency and resource allocation problem by considering the dual objective of (a) reducing passenger waiting times at stops and (b) reducing operational costs. The bus allocation problem for existing and short-turning/interlining lines is modeled as a combinatorial, constrained and multi-objective optimization problem that has an exponential computational complexity and a large set of decision variables due to the additional set of short-turning/interlining options. This constrained optimization problem is approximated with an unconstrained one with the use of exterior point penalties and is solved with a Genetic Algorithm (GA) meta-heuristic. The modeling approach is applied to the bus network of The Hague with the use of General Transit Feed Specification (GTFS) data and Automated Fare Collection (AFC) data from 24 weekdays. Sensitivity analysis results demonstrate a significant reduction potential in passenger waiting time and operational costs with the addition of only a few short-turning and interlining options.
机译:高峰时期的城市公共交通运营的特点是需求分布高度不均和资源稀缺。在这项工作中,我们提出了一种基于规则的方法,通过考虑以下双重目标,系统地生成和整合替代的衬砌方案(例如,短弯线和衬砌线)到频率和资源分配问题中:(a)减少乘客等待时间停止;(b)降低运营成本。现有和短弯/内衬线的公交车分配问题被建模为组合的,受约束的多目标优化问题,由于附加的短弯/内衬组,该问题具有指数级的计算复杂性和大量决策变量选项。通过使用外部点罚分,可以用无约束的问题近似该有约束的优化问题,并通过遗传算法(GA)元启发式算法解决。该建模方法通过使用24天以来的通用运输提要规范(GTFS)数据和自动票价收集(AFC)数据而应用于海牙的公交网络。敏感性分析结果表明,仅增加了一些短途转机和中转选项,可以显着减少乘客的等待时间和运营成本。

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