User-Oriented Last-Mile Transit Service Design: A Strategy for Behavioral Perception and Experience Optimization

Abstract

As a critical component of urban public transit systems, the underdeveloped state of last-mile transportation has become a bottleneck constraining service levels and sustainable development. Existing service models often overlook the dynamic and heterogeneous nature of user demand, leading to poor user experience and low operational efficiency. To address this challenge, this study proposes a new user-oriented paradigm for last-mile transit service design, which deeply integrates user behavior perception and service experience optimization. Specifically, we first leverage multi-source urban transit data, such as smart cards and shared mobility records, to construct a multi-dimensional user travel behavior perception model for accurately profiling the travel characteristics and preferences of different user groups. On this basis, we quantify the key factors affecting user experience by building a Structural Equation Model (SEM) and design personalized and dynamic service optimization strategies using multi-objective optimization algorithms. Simulation experiments on a real-world dataset show that the proposed service design method can increase user satisfaction by 15% and improve operational efficiency by 10% compared to traditional fixed-route models. The study validates that safety, convenience, and comfort are core elements influencing user satisfaction. This research provides a theoretical framework and practical guidance for designing more adaptive, efficient, and human-centric last-mile transit services, holding significant theoretical and practical value for promoting the construction of people-oriented, sustainable, and smart urban mobility ecosystems.