Microtransit revenue management informed by citywide travel demand and joint subscription–mode choice modeling

Xiyuan Ren, Joseph Y.J. Chow, Venktesh Pandey and Linfei Yuan

Transportation Research Part A: Policy and Practice, 2026, vol. 210, issue C

Abstract: As an IT-enabled multi-passenger mobility service, microtransit can improve accessibility, reduce congestion, and promote sustainability. However, realizing its business potential requires a deeper understanding of traveler preferences, highlighting the need for more effective tools for demand forecasting and revenue management, especially when actual usage data are limited. We propose an innovative modeling approach that integrates travel behavioral insights into microtransit policymaking. The approach operates by (1) leveraging citywide synthetic data to achieve greater spatiotemporal granularity, (2) estimating a nonparametric nested model for joint travel mode and ride-pass subscription choices, and (3) employing a simulation-based method to calculate revenue and traveler benefits under various policy scenarios. We demonstrate the applicability of our approach through a case study in Arlington, TX, one of the largest deployments of microtransit (Via) in the U.S. Using the simulation-based workflow, we evaluate alternative policy scenarios, including ride-pass discounts, event-based subsidies, and place-based subsidies, to assess their impacts on microtransit ridership, system revenue, and traveler welfare. The results indicate that reducing the weekly pass price from $25 to $18.9 and the monthly pass price from $80 to $71.5 would increase total revenue by approximately $127 per day. A 100% trip fare discount could reduce 61 car trips to AT&T Stadium during a game event while generating an additional 82 microtransit trips per day to Medical City Arlington. However, achieving these mode shifts would require subsidies of approximately $533 per event and $483 per day, respectively. A sensitivity analysis reveals that the framework’s predictions remain robust at the current adoption level, but accuracy becomes increasingly consequential as market share grows. The findings offer practical insights for microtransit deployment in Arlington, and the proposed framework is readily transferable to other cities with similar microtransit services.

Keywords: Microtransit; Revenue management; Travel behavior; Synthetic data; Arlington Via (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1016/j.tra.2026.105046

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