Digital platforms have improved the efficiency and quality of smart city operations by soliciting more customer inputs, for example, in the form of suggestions. One innovative option in urban transportation is the shared shuttle service, which lies between traditional public transportation and ride-hailing services. Platforms that offer these services can gather customer suggestions in a “crowd-starting” manner, which provides valuable insights into customer needs. However, this also presents a challenge in balancing service coverage and quality to meet customer needs implied by their suggestions. To address this issue, we introduce an optimization framework designed to maximize expected profit by leveraging customer response models which characterize how customers will respond to different service attributes and how their suggestions inform these responses. When estimating these response models, we present methods involving isotonic penalty and shrinkage tailored for handling small datasets. To demonstrate the practical implications, we apply our model to a shared shuttle service case study and discuss practical considerations, such as the value of information, the effectiveness of our estimation approaches, and the benefits of involving customers in the service design process.
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