MICROTRIPS: MICRO-geography TRavel Intelligence and Pattern Synthesis

• URL: http://arxiv.org/abs/2510.05080v1
• Date: Mon, 06 Oct 2025 17:50:56 GMT
• Title: MICROTRIPS: MICRO-geography TRavel Intelligence and Pattern Synthesis
• Authors: Yangyang Wang, Tayo Fabusuyi,
• Abstract summary: This study presents a novel small-area estimation framework to enhance urban transportation planning through detailed characterization of travel behavior.<n>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.
• Score: 1.884314837105005
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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