Bus stops vary widely in the conditions they provide to waiting passengers, yet ridership analysis rarely considers these differences alongside service performance. This study examines how stop-level amenities relate to bus ridership, conditional on service frequency and schedule deviation. Using bus stop amenity inventories, operational data, and stop-level boarding figures from Boston, USA, we have estimated a structural equation path model for a sample of continuously operated, AVL-equipped timepoint stops. The model uses observed composite scores for physical amenities at the stop level and accounts for headway and schedule deviation. The results indicate that greater provision of basic amenities is consistently linked to higher ridership, and during peak periods both amenity and service reliability measures show stronger associations. Further, spatial clustering analysis shows that poor waiting conditions are geographically concentrated rather than being randomly distributed. Taken together, the findings show how passenger waiting environments and service performance jointly influence ridership and point to opportunities for targeted investment strategies.

Urban mobility is a critical contributor to greenhouse gas emissions, accounting for over 30% of urban carbon emissions in the United States in 2021. Addressing this challenge requires a comprehensive and data-driven approach to transform transportation systems into sustainable networks. This paper presents an integrated framework that leverages artificial intelligence (AI), machine learning (ML), and life cycle assessment (LCA) to analyze, model, and optimize urban mobility. The framework consists of four key components: AI-powered analysis and models, synthetic urban mobility data generation, LCA for environmental footprint analysis, and data-driven policy interventions. By combining these elements, the framework not only deciphers complex mobility patterns but also quantifies their environmental impacts, providing actionable insights for policy decisions aimed at reducing carbon emissions and promoting sustainable urban transportation. The implications of this approach extend beyond individual cities, offering a blueprint for global sustainable urban mobility.