A data fusion approach for mobility hub impact assessment and location selection: integrating hub usage data into a large-scale mode choice model

Abstract

As cities grapple with traffic congestion and service inequities, mobility hubs offer a scalable solution to align increasing travel demand with sustainability goals. However, evaluating their impacts remains challenging due to the lack of behavioral models that integrate large-scale travel patterns with real-world hub usage. This study presents a data fusion approach that incorporates observed mobility hub usage into a mode choice model estimated with synthetic trip data. We identify trips potentially affected by mobility hubs, introduce a nested choice structure that accounts for mode transfers, and calibrate hub-specific parameters using on-site survey data and ground truth trip counts. A sensitivity analysis demonstrates that the calibration remains robust when the share of hub trips is relatively low and travel demand spans multiple OD pairs. We apply this approach to a case study in Capital District, NY, using data from a survey conducted by the Capital District Transportation Authority (CDTA) and a mode choice model estimated with Replica Inc.'s synthetic data. A bootstrap procedure quantifies uncertainty in hub usage and all downstream impact estimates. The two implemented hubs, near UAlbany Downtown Campus and in Downtown Cohoes, are projected to generate 9.89 (95% CI: [3.20, 29.04]) and 6.98 ([3.86, 12.67]) multimodal trips per day, reduce daily vehicle-miles-traveled (VMT) by 43.29 ([11.69, 142.81]) and 32.12 ([1.04, 60.27]) miles, and increase daily consumer surplus by $3,870 ([2,020, 5,276]) and $1,790 ([1,202, 2,068]), respectively. A regional evaluation of 1,100 candidate locations highlights that optimal hub siting varies by planning objective, with hubs along intercity corridors and urban peripheries yielding the largest behavioral impacts.