Joint Optimization of Bus Scheduling and Targeted Bus Exterior Advertising
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 5
Abstract
Bus exterior advertising provides a powerful way to establish brand awareness because it can reach a mass of audiences with a high frequency. For a certain advertisement category, advertising effectiveness is largely dependent upon its exposure times to the target audience who takes interest in advertisement, which is termed targeted advertising. Given that the distribution of target audiences over a city varies among different advertisement categories, a practical way of enhancing overall advertising effectiveness is to deploy a bus with certain advertisement category to the bus line that best fits its target area. This gives rise to a decision-making problem of targeted bus exterior advertising and bus scheduling. In this paper, the problem is formulated as a biobjective optimization model with objectives of maximizing the quantified advertising effectiveness and minimizing the number of bus fleet size to cover all trips. The advertising effectiveness is quantified using audience demographic data. The deadheading of buses is also enabled in the scheduling process to facilitate both objectives. The Non-dominated Sorting Genetic Algorithm-II-Large Neighborhood Search (NSGA-II-LNS) algorithm is developed to solve the biobjective problem with the incorporation of large neighborhood search operators into the framework of the NSGA-II to improve solution quality. Various experiments were set up to verify the proposed model and solution algorithm.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 5 • May 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Nov 12, 2022
Accepted: Dec 29, 2022
Published online: Feb 21, 2023
Published in print: May 1, 2023
Discussion open until: Jul 21, 2023
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