MICROTRIPS: MICRO-geography TRavel Intelligence and Pattern Synthesis
This enables granular insights for localized interventions like curb-space management and inclusive transportation solutions, though it is incremental in improving existing travel models.
The study tackles urban transportation planning by developing a small-area estimation framework that uses microdata and machine learning to predict travel behavior at high resolution, achieving higher accuracy than conventional approaches in validation with ACS/PUMS datasets.
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.