Multimodal Trip-Chain Planner for Incentivizing Transit Usage [supplemental files]

Details

• Creators:
  Pandey, Venktesh ; Chow, Joseph ; Park, Hyoshin ; Bikdash, Marwan ; Tiamiyu, Ridwan ; Ren, Xiyuan ; Jehu-Appiah, Doreen ; Sundaram, Indramuthu Georgewilliam ; Liu, BingQing
• Corporate Creators:
  Connected Communities for Smart Mobility Toward Accessible and Resilient Transportation for Equitably Reducing Congestion (C2SMARTER) Tier-1 University Transportation Center (UTC) ; New York University. Tandon School of Engineering
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology ; United States. Department of Transportation. Federal Highway Administration
• Subject/TRT Terms:
  Incentives Mobility Pricing Public Transit Trip Chaining
• Resource Type:
  Dataset
• Right Statement:
  Open Access; https://creativecommons.org/licenses/by/4.0/
• Geographical Coverage:
  United States
• Corporate Publisher:
  Connected Communities for Smart Mobility Toward Accessible and Resilient Transportation for Equitably Reducing Congestion (C2SMARTER) Tier-1 University Transportation Center (UTC)
• Abstract:
  As an IT-enabled multi-passenger mobility service, microtransit can improve accessibility, reduce congestion, and enhance flexibility. However, its heterogeneous impacts across travelers necessitate better tools for microtransit forecasting and revenue management, especially when actual usage data are limited. We propose a nested nonparametric model for joint travel mode and ride pass subscription choice, estimated using marginal subscription data and synthetic populations. The model improves microtransit choice modeling by (1) leveraging citywide synthetic data for greater spatiotemporal granularity, (2) employing an agent-based estimation approach to capture heterogeneous user preferences, and (3) integrating mode choice parameters into subscription choice modeling. We apply our methodology to a case study in Arlington, TX, using synthetic data from Replica Inc. and microtransit data from Via. Our model accurately predicts the number of subscribers in the upper branch and achieves a high McFadden R2 in the lower branch (0.603 for weekday trips and 0.576 for weekend trips), while also retrieving interpretable elasticities and consumer surplus. We further integrate the model into a simulation-based framework for microtransit revenue management. For the ride pass pricing policy, our simulation results show that reducing the price of the weekly pass (18.9) and monthly pass (71.5) would surprisingly increase total revenue by 533 per event and $483 per day, respectively.
• Content Notes:
  This dataset additional data and software in a GitHub repository. This repository is accessible online: https://github.com/tml-ncat/Multimodal-Trip-Planner
• Format:
  ZIP
• Funding:
  69A3552348326
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:36d493f656c6dd97ee080d38619fdb97e63eb1dce5f2f12b9483ae98cc401af286ffe63f3de51ac70bf8b0a5daf2061f8146c72161d687a0d93b5d95755696ef
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/86432/dot_86432_DS1.zip
• File Type:
  [ZIP - 12.04 MB ]