Integrated design and optimization of customized bus travel services for urban commuting

Customized bus (CB) travel services present a promising solution for urban transportation, blending the convenience of private cars with the cost efficiency of public transit. This study proposes a comprehensive CB service system for urban commuting, optimizing both station locations and scheduling strategies. First, K-means clustering algorithm is applied to determine bus station locations based on the spatical distribution of commuting demand. The bus routing problem is then formulated as a mixed-integer nonlinear programming (MINLP) model, considering both system costs and passenger waiting times. To efficiently solve the MINLP model in urban scenarios, an adaptive large-scale neighborhood search (ALNS) algorithm is employed. Finally, the proposed system is validated using real-world data. Results indicate that the CB service notably reduces average passenger waiting time and vehicle travel time compared to traditional taxi services, while also achieving over a 30% reduction in operating costs.