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<p>This study addresses the critical challenge of optimizing parking pricing, demand, and supply in parking management systems. While previous studies have emphasized the role of dynamic pricing in traffic management, this research offers a new perspective by applying competitive game theory. By incorporating customer preferences and strategic interactions between parking agents, we address a key limitation in existing research. By considering parking agents as rational entities seeking to maximize their profits, we develop a bilevel optimization model that captures the interplay between demand, pricing strategies, and parking lot capacities. Our model leverages evolutionary algorithms to solve the optimization problem and provides valuable insights into the factors influencing parking lot profits.To evaluate the performance of our proposed model, we conducted extensive simulations using hypothetical and randomly generated data to achieve optimal pricing strategies and maximizes revenue for parking agents.</p>

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Dynamic vehicle parking pricing: a bilevel optimization approach

  • 期刊论文

  • 发表日期：

    2025年1月29日

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  • Semeneh Hunachew Bayih,
  • Surafel Luleseged Tilahun

摘要

This study addresses the critical challenge of optimizing parking pricing, demand, and supply in parking management systems. While previous studies have emphasized the role of dynamic pricing in traffic management, this research offers a new perspective by applying competitive game theory. By incorporating customer preferences and strategic interactions between parking agents, we address a key limitation in existing research. By considering parking agents as rational entities seeking to maximize their profits, we develop a bilevel optimization model that captures the interplay between demand, pricing strategies, and parking lot capacities. Our model leverages evolutionary algorithms to solve the optimization problem and provides valuable insights into the factors influencing parking lot profits.To evaluate the performance of our proposed model, we conducted extensive simulations using hypothetical and randomly generated data to achieve optimal pricing strategies and maximizes revenue for parking agents.