Guiding Self-Organizing Dynamics of Residential Choice in Cities to Reduce Traffic Congestion and Carbon Emissions

Abstract

Rapid urbanization and growing vehicle ownership exacerbate traffic congestion and prolong commute times. We examine the self-organizing dynamics of residential choice via a hypothetical home-swapping process to mitigate peak-hour traffic congestion and carbon emissions. Specifically, we analyze over 400,000 trajectories from 9 days in a major Chinese city, revealing that actual average commuting distance is approximately three times shorter than under random residential distribution, indicating significant self-organization. Notably, city-wide home swapping reduces commuting distance by 50.4%, substantially easing traffic congestion, thereby reducing carbon emissions by 77.3%. Even with the consideration of socio-demographic factors and individual needs, the reductions remain significant: 8.1%-10.3% in commuting distance and 27.4%-34.4% in carbon emissions. Considering the potential induction of additional non-commuting trips, the reduction in carbon emissions remains substantial. Given the primacy of distance to the city center, polycentric city layouts can enhance these benefits. For validation, we use another dataset covering China's 28 major cities to confirm these findings. Finally, we introduce a data-driven model to elucidate self-organizing dynamics of residential choice and analyze the feasibility of government coordination. These insights demonstrate that a synergistic alignment of residential choices can leverage individual and city-level benefits, effectively alleviating commuting congestion and associated emissions.