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Mode Split Equilibrium Microsimulation Approach for Early-Stage On-Demand Shared Automated Mobility.

Lei Zhu1, Jinghui Wang2, Yuqiu Yuan1

  • 1Department of Systems Engineering and Engineering Management, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.

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Summary
This summary is machine-generated.

Deployment of shared automated vehicles (SAVs) in Automated Mobility Districts (AMDs) is feasible. A new model predicts SAV mode split and service levels, aiding early-stage planning for these innovative transit solutions.

Keywords:
automated mobility districtequilibriummode choiceshared mobilitysimulation

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Area of Science:

  • Transportation Engineering
  • Urban Planning
  • Simulation Modeling

Background:

  • Automated Vehicle (AV) technology deployment is shifting towards low-speed, shared automated shuttles in dense urban areas, termed Automated Mobility Districts (AMDs).
  • Effective planning tools are needed to assess the viability and impact of SAV services within these specific districts.

Purpose of the Study:

  • To develop and present an integrated framework combining multi-mode choice and microscopic traffic simulation.
  • To determine the equilibrium mode split for various transportation modes, including SAVs, within AMDs.
  • To evaluate the level-of-service and network performance resulting from predicted SAV adoption.

Main Methods:

  • An integrated framework merging multi-mode choice models with microscopic traffic simulation was developed.
  • The framework utilizes real-time traffic simulation data to achieve mode split equilibrium.
  • The model was validated using travel demand and network data from Greenville, South Carolina, considering car, walk, and two SAV modes.

Main Results:

  • The proposed framework effectively determined the mode split equilibrium for shared automated vehicles in an Automated Mobility District.
  • Sensitivity analyses revealed the impact of waiting times and fleet size on SAV mode share.
  • The study demonstrated the framework's capability to predict service levels and network performance based on SAV integration.

Conclusions:

  • The developed integrated framework is a valuable decision-support tool for early-stage planning of Automated Mobility Districts.
  • Understanding factors influencing mode split is crucial for optimizing SAV deployment and ensuring successful integration into urban mobility.
  • This research provides a robust methodology for assessing the potential of shared automated mobility in future urban transportation systems.