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access icon free Optimising departure intervals for multiple bus lines with a multi-objective model

© The Institution of Engineering and Technology
Received 16/02/2017, Accepted 17/04/2018, Revised 26/03/2018, Published 26/04/2018

A multi-objective model was developed to optimize departure intervals synchronously for multiple bus lines. Then, a Genetic Algorithm with “Elitist Preservation” strategy combining the economical method of “dynamic scoring” (GA-EPDS) was proposed to solve the multi-objective model. The proposed method included three objectives: the first objective was to maximize the bus operation profits; the second objective was to minimize the passengers’ transfer waiting time; and the last one was to minimize passengers’ costs. Transfer waiting time was crucial for multiple bus lines and long transfer waiting time would decrease the satisfaction of passengers, so transfer waiting time was regarded as a single objective. In addition, an evaluation function, which was obtained through a “dynamic scoring” method, was formulated to estimate whether the three objective functions reached a global optimum. In order to improve the solution generated in terms of computational effort and convergence, a GA-EPDS was designed to solve the multi-objective model. Finally, the proposed approach was applied in a case study of an actual network. The numerical results based on different scale instances and different traffic conditions demonstrate that our proposed model and method are effective and feasible to optimize departure intervals for multiple bus lines.

Inspec keywords: minimisation; road vehicles; road traffic; genetic algorithms

Other keywords: bus operation profit maximization; genetic algorithm; elitist preservation strategy; passenger transfer waiting time; passenger satisfaction; departure interval optimization; global optimum; traffic conditions; evaluation function; GA-EPDS; passenger cost minimization; dynamic scoring method; multiobjective model; multiple bus lines

Subjects: Systems theory applications in transportation; Optimisation techniques

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