The booming crowdsourced delivery leverages networks of local, part time couriers to meet growing customer demands for increasingly speedy shipping. By conducting city-wide low-cost delivery networks, crowd-sourced enabled instant delivery platforms have fought to figure out a way to offer instant shipping at low price. Nevertheless, limited ubiquitous computing infrastructures impede the discovery of equilibrium between cost reduction and sufficient shipping capacity. In this article, we present our endeavor towards understanding and designing a dynamic pricing framework based on a large-scale food-delivery platform. To reduce delivery fees in capacity overload regions and resolve the decline of service quality caused by insufficient shipping capacity, we address the challenge of quantifying the inter-regional shipping capacity disparities by accordingly applying a variety of techniques, including cooperative game theory, probability analysis and address entity recognition. In particular, we provide an$O(n^{2})$algorithm for the cooperative game based cost allocation, of which the exponential computational complexity$O(n^{2}2^{n})$is generally considered a major challenge in real-world applications. City-wide experiments in Nanjing and Shenzhen illustrate that we achieve a 10% decrease in delivery fees under overload shipping capacity and a 3% increase in completion rates in customer locations with poor service quality.