Microtransit revenue management informed by citywide travel demand and joint subscription-mode choice modeling
Xiyuan Ren, Joseph Y. J. Chow, Venktesh Pandey, Linfei Yuan

TL;DR
This paper introduces a novel demand forecasting and revenue management approach for microtransit, integrating citywide data and behavioral modeling to optimize policies and improve system performance.
Contribution
It develops an innovative, simulation-based framework that combines travel behavior insights with synthetic data for microtransit policy evaluation.
Findings
Reducing pass prices increases revenue and ridership.
Subsidies can significantly shift travel modes and reduce car trips.
Policy simulations identify cost-effective strategies for microtransit enhancement.
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…
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