Data-Driven Urban Decentralization: Modeling Mode and Destination Choice in the X-Minute City via Stated Preferences

Abstract

There are significant social, environmental, and public health advantages to encouraging cycling over automobile transportation. Although the "x-minute city" concept aims to decentralize urban services within a short bike ride or walk, there is currently little empirical modeling of how this framework affects individual behavioral choices. This study assesses the effect of proximity on bicycle adoption using intelligent urban modeling. We generated Multinomial Logit (MNL) models to examine mode and destination choice dynamics using two stated preference experiments conducted through a face-to-face survey (N=390) in Kerman, Iran. Travelers are successfully diverted from the city center by creating decentralized, replicated urban services within a 10-minute cycling radius, according to the predictive analytics. When combined with push-factors like congestion pricing and smart infrastructure expenditures like dedicated parking and bike lanes, this behavioral shift is further enhanced. For transportation planners and legislators using data intelligence to create sustainable, polycentric smart cities, these results offer crucial quantitative insights.