An Optimization-Based Planning Tool for On-Demand Mobility Service Operations

H. M. Abdul Aziz, Venu Garikapati, Tony Rodriguez, Lei Zhu, Bingrong Sun, Stanley Young, Yuche Chen

Research output: Contribution to journalArticlepeer-review

5 Scopus Citations

Abstract

Regions worldwide are adopting and exploring low-speed automated electric shuttle (AES) service as an on-demand shared mobility service in dense geofenced urban areas. Building on this concept, the National Renewable Energy Laboratory (NREL) recently developed the Automated Mobility District (AMD) toolkit. The AMD toolkit—comprising of a travel micro-simulation model and an energy estimation model—estimates the mobility and energy impacts of a given shuttle configuration within an AMD. Early-stage AMD deployments need to find optimal operational configurations that include: (a) passenger capacity of an AES, (b) time-dependent routes, and (c) fleet size (AES units) to satisfy the demand for the region. This research extends the AMD toolkit functionality by developing an optimization-based planning module that will assist in the operations of AES units. We developed a constrained mixed-integer program accounting for passenger waiting time, battery range, and passenger capacity of AES units. For scalability, we demonstrated the Tabu search-based solution technique for a real-world network—a proposed AMD deployment in Greenville, South Carolina, USA. Compared to rule-based operations, our developed solution yields higher travel time and energy savings for the network at different demand levels. The sensitivity analyses for waiting time thresholds indicate nonlinearity in the system performance, underscoring the need to meet shared-use mobility user-level expectations. The developed optimization framework can be adapted and extended to accommodate different categories of shared-use on-demand mobility services.

Original languageAmerican English
Pages (from-to)45-56
Number of pages12
JournalInternational Journal of Sustainable Transportation
Volume16
Issue number1
DOIs
StatePublished - 2022

Bibliographical note

Publisher Copyright:
© 2020 Taylor & Francis Group, LLC.

NREL Publication Number

  • NREL/JA-5400-78108

Keywords

  • automated mobility district
  • Autonomous shuttle
  • on-demand mobility
  • shared mobility
  • Tabu search
  • vehicle routing

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