The Impact of Public Transportation Development on Convenience Store Revenue and Countermeasures in Shanghai
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Abstract
Background and Aim: The rapid expansion of convenience stores in China has intensified market competition, making strategic location selection crucial for retail success. This study examines Lawson’s expansion strategy in China within the framework of the “public transportation priority” policy, focusing on how transportation infrastructure influences competitive advantage. The research aims to identify key factors in site selection and develop a genetic algorithm-based optimization model to enhance store layout decisions in dynamic urban environments.
Materials and Methods: Mobile signaling base station data from Shanghai’s Jiading District were analyzed to map population distribution using location updates, call records, and data exchanges. After data cleaning and spatial quantization, heat maps and spatial models were generated. A Stochastic Utility Model, integrated with a genetic algorithm, was employed to optimize site selection based on population density, proximity to subway stations, and budget constraints.
Results: Findings indicate that prioritizing high-traffic subway stations significantly enhances consumer footfall and profitability. Heat maps revealed strong correlations between population clusters, transportation hubs, and optimal store locations. The genetic algorithm-based model demonstrated superior efficiency in balancing cost constraints with revenue potential, reinforcing the role of transportation infrastructure in retail site selection.
Conclusion: Public transportation accessibility, particularly subway proximity, is a decisive factor in convenience store success. The proposed genetic algorithm model offers a scalable tool for retailers to adapt to evolving urban transportation systems. These findings underscore the importance of integrating transportation data into retail planning, providing actionable insights for market expansion strategies in China and other high-density regions.
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