Rider Pressure in Intelligent Food Delivery Considering Dynamic Weather in Smart Cities

To address the imbalance among riders, platforms, and merchants in food delivery within the context of Smart Cities, this study proposes an Intelligent Food Delivery model incorporating Dynamic Weather Conditions. A multi-objective optimization framework considering customer satisfaction, rider delivery pressure, and platform efficiency is established. An Improved Sparrow Search Algorithm (ISSA), enhanced by Sin-chaotic, Golden Sine, Adaptive t-distribution, and Dynamic Selection mechanisms, demonstrates superior convergence speed and accuracy. Simulations under Clear, Rainy, and Snowy scenarios validate ISSA's effectiveness in reducing rider pressure and optimizing delivery routes, providing robust theoretical guidance for intelligent logistics management in Smart Cities.