The First Mile–Last Mile (FMLM) connectivity is crucial for improving public transit accessibility and efficiency, particularly in sprawling suburban regions where traditional fixed-route transit systems are often inadequate. Autonomous on-Demand Shuttles (AODS) hold promise as an option for FMLM connections because of their cost-effectiveness and improved safety features, thereby enhancing user convenience and reducing reliance on personal vehicles. A critical issue in AODS service design is the optimization of travel paths, for which realistic traffic network assignment combined with optimal routing offers a viable solution. In this study, we have designed an AODS controller that integrates a mesoscopic simulation-based Dijkstra shortest path algorithm with a greedy and exhaustive insertion heuristics approach to optimize the travel routes of the shuttles. The controller also considers the charging infrastructure/strategies and the impact of the shuttles on regular traffic flow for routes and fleet-size planning. The controller is implemented in the Aimsun Next traffic simulator considering Lake Nona in Orlando, Florida as a case study. We show that, under the present demand based on 1% of total trips as transit riders, a fleet of three autonomous shuttles can serve about 80% of FMLM trip requests on an on-demand basis with an average waiting time below 4 min. Additional power sources have a significant effect on service quality as the inactive waiting time for charging would increase the fleet size. We also show that low-speed autonomous shuttles would have negligible impact on regular vehicle flow, making them suitable for suburban areas. These findings have important implications for sustainable urban planning and public transit operations.

Autonomous shuttle services are currently introduced in different urban conditions throughout the world. As a result, studies are needed to assess the safety and mobility performance of such autonomous shuttle services. However, calibrating the movement of autonomous shuttles in a simulation environment has been a difficult task due to the absence of any real-world data. This study aims to assess the impact of the shuttle service on suburban road capacity through a calibrated simulation prototype of an operational shuttle system at Lake Nona, Orlando, Florida. The movements of autonomous vehicles are calibrated using real-world trajectory data, which helps replicate the driving behavior of the shuttle in the simulation. The analysis reveals that with increasing frequency of the shuttle service, the delay time percentage of the shared road sections increases and traveling speed decreases. A moving bottleneck phenomenon is also observed with the shuttle movement. The findings also demonstrate that increasing the speed of shuttles up to 10 mph can improve traffic conditions, which are constrained by the operational safety aspects. The findings from this study provide insights for policymakers and transportation agencies towards deploying autonomous shuttles and for planning road infrastructures for shared road-use of autonomous and human-driven vehicles.

The First-Mile Last-Mile (FMLM) connectivity is crucial for improving public transit accessibility and efficiency, particularly in sprawling suburban regions where traditional fixed-route transit systems are often inadequate. Autonomous on-Demand Shuttles (AODS) hold a promising option for FMLM connections due to their cost-effectiveness and improved safety features, thereby enhancing user convenience and reducing reliance on personal vehicles. A critical issue in AODS service design is the optimization of travel paths, for which realistic traffic network assignment combined with optimal routing offers a viable solution. In this study, we have designed an AODS controller that integrates a mesoscopic simulation-based dynamic traffic assignment model with a greedy insertion heuristics approach to optimize the travel routes of the shuttles. The controller also considers the charging infrastructure/strategies and the impact of the shuttles on regular traffic flow for routes and fleet-size planning. The controller is implemented in Aimsun traffic simulator considering Lake Nona in Orlando, Florida as a case study. We show that, under the present demand based on 1% of total trips as transit riders, a fleet of 3 autonomous shuttles can serve about 80% of FMLM trip requests on-demand basis with an average waiting time below 4 minutes. Additional power sources have significant effect on service quality as the inactive waiting time for charging would increase the fleet size. We also show that low-speed autonomous shuttles would have negligible impact on regular vehicle flow, making them suitable for suburban areas. These findings have important implications for sustainable urban planning and public transit operations.

The shared operational characteristics of first-mile/last-mile passenger services and last-mile parcel deliveries present the transportation planners with a significant opportunity to integrate these two services into a single, unified system to save energy and reduce both VMT and emissions. With advancements in vehicle automation, the autonomous on-demand shuttles (AODS) have emerged as a promising alternative for this integrated service due to their lower operating costs, reduced emissions, and enhanced safety features. In this study, we develop a dynamic controller to manage the integrated operations of passenger and parcel delivery services with AODS fleet in dynamic traffic scenarios. The integrated controller is connected to a mesoscopic dynamic traffic simulator using Aimsun Ride API. It also includes a fleet schedule optimization module based on guided local search metaheuristics and implemented through Google OR-Tools. The framework is implemented as a case study for the Lake Nona suburban community of the Central Florida region containing about 2200 households and its efficiency is evaluated across varying demand levels (different passenger and parcel request scenarios) and operational configurations (fleet sizes and service modes—mixed vs. separate). The experiments demonstrate that on average mixed passenger–parcel service fleets can significantly increase service efficiency with about 1.3 percentage points higher fleet utilization ratio and 3.7 percentage points lower empty vehicle travel ratio than separate fleets. This study provides insights on the potential of an integrated passenger transport and parcel delivery service with AODS to improve urban mobility and logistics by reducing VMT and energy consumption.