Rethinking Future Travel Demand in China: Integrating IAM with Local Context for Sustainable Future Mobility

Urban mobility is undergoing significant technological transformations, with the application of sharing and autonomous driving technologies. It will reshape people's travel behavior patterns. However, the direction of the change is heavily influenced by urban spatial features, including the density of population, the distance between residents and job, the public transportation infrastructure, the diversity of local place, as well as the urban form. In response to this evolving landscape, this study integrates macro-level predictions from IAM with micro-level features of urban space to reassess the trends in travel demand in China up to the years 2030 and 2060. The findings indicate that, when considering the micro-features of existing urban spaces, projections based on future comprehensive system evaluation models may significantly overestimate the volume of car travel, so as to the demands on private cars. Variations between different regions and within the same city, particularly between new and old neighborhoods, further reveal the substantial potential for reducing car travel through urban planning and management. Consequently, this research proposes the design and experimentation of new business models for intelligent and shared mobility that align with the micro-spatial configuration of cities. It explores more sustainable pathways for the low-carbon transformation of urban transportation, aiming to harness the unique spatial attributes of cities to foster innovative solutions.