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The Impact of Shared Autonomous Vehicles in Microtransit Systems: A Case Study in Atlanta

Jason Lu, Tejas Santanam, Hongzhao Guan, Connor Riley, Meen-Sung Kim, Anthony Trasatti, Neda Masoud, Pascal Van Hentenryck

TL;DR

The paper investigates how shared autonomous vehicles (SAVs) influence performance in microtransit within an ODMTS framework by introducing URDC, a three-part dispatch/control stack (RTDARS, RTDARS-SAV, and MPC) that can rebalance shuttles without heavy reliance on historical data. Using MARTA Reach Atlanta pilot data, the study demonstrates that SAVs decrease passenger waiting times, maintain travel times, and reduce total and empty miles, with the greatest gains when combined with strategic idle-stop relocation. The work provides a data-light optimization approach and a rigorous, scenario-driven evaluation across four autonomy levels, fleet sizes, and ridership scenarios, offering actionable insights on fleet planning, routing, and operating costs. Overall, the findings support the viability and cost-effectiveness of SAV-enabled ODMTS in urban settings and highlight directions for future research, including electric vehicle integration and broader multimodal incentives.

Abstract

Microtransit systems represent an enhancement to solve the first- and last-mile problem, integrating traditional rail and bus networks with on-demand shuttles into a flexible, integrated system. This type of demand responsive transport provides greater accessibility and higher quality levels of service compared to conventional fixed-route transit services. Advances in technology offer further opportunities to enhance microtransit performance. In particular, shared autonomous vehicles (SAVs) have the potential to transform the mobility landscape by enabling more sustainable operations, enhanced user convenience, and greater system reliability. This paper investigates the integration of SAVs in microtransit systems, advancing the technological capabilities of on-demand shuttles. A shuttle dispatching optimization model is enhanced to accommodate for driver behavior and SAV functionalities. A model predictive control approach is proposed that dynamically rebalances on-demand shuttles towards areas of higher demand without relying on vast historical data. Scenario-driven experiments are conducted using data from the MARTA Reach microtransit pilot. The results demonstrate that SAVs can elevate both service quality and user experience compared to traditional on-demand shuttles in microtransit systems.

The Impact of Shared Autonomous Vehicles in Microtransit Systems: A Case Study in Atlanta

TL;DR

The paper investigates how shared autonomous vehicles (SAVs) influence performance in microtransit within an ODMTS framework by introducing URDC, a three-part dispatch/control stack (RTDARS, RTDARS-SAV, and MPC) that can rebalance shuttles without heavy reliance on historical data. Using MARTA Reach Atlanta pilot data, the study demonstrates that SAVs decrease passenger waiting times, maintain travel times, and reduce total and empty miles, with the greatest gains when combined with strategic idle-stop relocation. The work provides a data-light optimization approach and a rigorous, scenario-driven evaluation across four autonomy levels, fleet sizes, and ridership scenarios, offering actionable insights on fleet planning, routing, and operating costs. Overall, the findings support the viability and cost-effectiveness of SAV-enabled ODMTS in urban settings and highlight directions for future research, including electric vehicle integration and broader multimodal incentives.

Abstract

Microtransit systems represent an enhancement to solve the first- and last-mile problem, integrating traditional rail and bus networks with on-demand shuttles into a flexible, integrated system. This type of demand responsive transport provides greater accessibility and higher quality levels of service compared to conventional fixed-route transit services. Advances in technology offer further opportunities to enhance microtransit performance. In particular, shared autonomous vehicles (SAVs) have the potential to transform the mobility landscape by enabling more sustainable operations, enhanced user convenience, and greater system reliability. This paper investigates the integration of SAVs in microtransit systems, advancing the technological capabilities of on-demand shuttles. A shuttle dispatching optimization model is enhanced to accommodate for driver behavior and SAV functionalities. A model predictive control approach is proposed that dynamically rebalances on-demand shuttles towards areas of higher demand without relying on vast historical data. Scenario-driven experiments are conducted using data from the MARTA Reach microtransit pilot. The results demonstrate that SAVs can elevate both service quality and user experience compared to traditional on-demand shuttles in microtransit systems.
Paper Structure (24 sections, 1 equation, 9 figures, 10 tables)

This paper contains 24 sections, 1 equation, 9 figures, 10 tables.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Contributions
  4. Unified Real-Time Dispatch and Control
  5. Preliminaries
  6. RTDARS
  7. Modeling Driver Behavior in RTDARS
  8. Model Predictive Control
  9. RTDARS-SAV
  10. Case Study
  11. Pilot Operations
  12. Experimental Scenarios
  13. Fleets
  14. Shuttle Functionality Levels
  15. Ridership Sampling
  16. ...and 9 more sections

Figures (9)

  • Figure 1: Example ODMTS with Passenger Path (solid lines)
  • Figure 2: Belvedere and West Atlanta Marta Reach zones (blue zones correspond to phase 1 deployment areas and pink zones correspond to phase 2 expansions of deployment areas)
  • Figure 3: Driver Response Time Distribution
  • Figure 4: Average Waiting Time Per Trip Across SFLs at Base Ridership Level
  • Figure 5: Average Travel Time Per Trip Across SFLs at Base Ridership Level
  • ...and 4 more figures