An optimization-based planning tool for on-demand mobility service operations

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.

Keywords:

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

Acknowledgments

This work was partially authored by the NREL, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE AC36-08GO28308. Funding provided by the DOE Vehicle Technologies Office (VTO) under the Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Laboratory Consortium, an initiative of the Energy Efficient Mobility Systems (EEMS) Program. The authors would particularly like to thank David Anderson and Prasad Gupte with DOE’s Office of Energy Efficiency and Renewable Energy (EERE) for helping to establish the project concept and advance its implementation, and for providing ongoing guidance and support. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes.

Authors’ contributions

The authors confirm contribution to the paper as follows: study conception and design: All; Optimization model: Aziz & Rodriguez; Literature Review: Aziz, Garikapati, and Sun, Data preparation: Zhu, Yuche, Aziz; analysis and interpretation of results: Aziz, and Garikapati; draft manuscript preparation: All. All authors reviewed the results and approved the final version of the manuscript.

Additional information

Funding

This work was partially authored by the NREL, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE AC36-08GO28308. Funding provided by the DOE Vehicle Technologies Office (VTO) under the Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Laboratory Consortium, an initiative of the Energy Efficient Mobility Systems (EEMS) Program. The authors would particularly like to thank David Anderson and Prasad Gupte with DOE’s Office of Energy Efficiency and Renewable Energy (EERE) for helping to establish the project concept and advance its implementation, and for providing ongoing guidance and support. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes.