MICROTRIPS: MICRO-geography TRavel Intelligence and Pattern Synthesis
Yangyang Wang, Tayo Fabusuyi
TL;DR
This work tackles the need for high-resolution travel demand estimates at small geographic scales to support urban planning. It extends the four-step travel model by integrating a synthetic population generated via Iterative Proportional Fitting ($IPF$) from public microdata, machine-learning trip-generation, entropy-based origin–destination distribution, Bayesian mode analysis, and multimodal routing. The approach is demonstrated in Seattle with ACS/PUMS validation, achieving higher accuracy than conventional methods and yielding granular, policy-relevant insights such as microtransit potential and curb-space optimization. By using synthetic data, it preserves privacy while enabling prescriptive scenario analysis, helping city planners design targeted, equity-conscious interventions and bridge regional models with neighborhood realities.
Abstract
This study presents a novel small-area estimation framework to enhance urban transportation planning through detailed characterization of travel behavior. Our approach improves on the four-step travel model by employing publicly available microdata files and machine learning methods to predict travel behavior for a representative, synthetic population at small geographic areas. This approach enables high-resolution estimation of trip generation, trip distribution, mode choice, and route assignment. Validation using ACS/PUMS work-commute datasets demonstrates that our framework achieves higher accuracy compared to conventional approaches. The resulting granular insights enable the tailoring of interventions to address localized situations and support a range of policy applications and targeted interventions, including the optimal placement of micro-fulfillment centers, effective curb-space management, and the design of more inclusive transportation solutions particularly for vulnerable communities.