ABSTRACT 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. INTRODUCTION High population and economic growths in the urban regions of the USA are leading to increased traffic congestion, environmental impacts, and crashes. To reduce traffic congestion and associated problems, it is important to increase the use of public transit services which constitute about 1% of the mode share in the USA (1).

Autonomous vehicles are expected to transform transportation systems with rapid technological advancement. Human mobility would become more accessible and safer with the emergence of driverless vehicles. To this end, 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 calibrate autonomous shuttles in a microscopic traffic simulation model and consequently assess the impact of the shuttle service on urban road capacity through simulation experiments. For this analysis, a prototype of an operational shuttle system at Lake Nona, Orlando, Florida is emulated in a microscopic traffic simulator during different times of the day. The movements of autonomous vehicles are calibrated using real-world trajectory data which help 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. It is also found that increasing the speed of shuttles up to 5 mph during off-peak hours and 10 mph during peak hours will improve traffic conditions. The findings from this study will assist policymakers and transportation agencies to revise policies for deploying autonomous shuttles and for planning road infrastructures for shared road-use of autonomous shuttles and human driven vehicles.

The increasing shortage of drivers poses a significant threat to vulnerable populations, particularly seniors and disabled individuals who heavily depend on public transportation for accessing healthcare services and social events. Autonomous Vehicles (AVs) emerge as a promising alternative, offering potential improvements in accessibility and independence for these groups. However, current designs and studies often overlook the unique needs and experiences of these populations, leading to potential accessibility barriers. This paper presents a detailed case study of an autonomous shuttle test specifically tailored for seniors and disabled individuals, conducted during the early stages of the COVID-19 pandemic. The service, which lasted 13 weeks, catered to approximately 1500 passengers in an urban setting, aiming to facilitate access to essential services. Drawing from the safety operator's experiences and direct observations, we identify critical user experience and safety challenges faced by vulnerable passengers. Based on our findings, we propose targeted initiatives to enhance the safety, accessibility, and user education of AV technology for seniors and disabled individuals. These include increasing educational opportunities to familiarize these groups with AV technology, designing AVs with a focus on diversity and inclusion, and improving training programs for AV operators to address the unique needs of vulnerable populations. Through these initiatives, we aim to bridge the gap in AV accessibility and ensure that these technologies benefit all members of society.

The European Green Deal aims to achieve climate neutrality by 2050, which demands improved emissions efficiency from the transportation industry. This study uses an agent-based simulation to analyze the sustainability impacts of shared autonomous shuttles. We forecast travel demands for 2050 and simulate regulatory interventions in the form of replacing private cars with a fleet of shared autonomous shuttles in specific areas. We derive driving-related emissions, energy consumption, and non-driving-related emissions to calculate life-cycle emissions. We observe reduced life-cycle emissions from 0.4% to 9.6% and reduced energy consumption from 1.5% to 12.2%.

Enhancing Campus Mobility: Achievements and Challenges of Autonomous Shuttle "Snow Lion" In recent years, the rapid evolution of autonomous vehicles (AVs) has reshaped global transportation systems. Leveraging the accomplishments of our earlier endeavor, particularly "Hercules" [1], an autonomous logistics vehicle for transporting goods, we introduce "Snow Lion", an autonomous shuttle vehicle meticulously designed to transform on-campus transportation, providing a safe and efficient mobility solution for students, faculty, and visitors. The main aim of this research is to improve campus mobility through a dependable, efficient, and eco-friendly autonomous transportation solution tailored to meet the diverse requirements of a university setting. This initiative significantly differs from the experiences of "Hercules" [1], as the campus environment presents a notable contrast to the structured environments of highways and urban streets. Emphasizing both security and passenger comfort, the primary focus is Figure 1: This figure illustrates the operational scenario of our on passenger transportation. Achieving this goal involves a autonomous shuttle during its service period at The Hong detailed examination of complex system designs that integrate Kong University of Science and Technology (Guangzhou) trajectory planning adjustments, prioritizing pedestrian safety (referred to as HKUST (GZ)).

Urban mobility is on the cusp of transformation with the emergence of shared, connected, and cooperative automated vehicles. Yet, for them to be accepted by customers, trust in their punctuality is vital. Many pilot initiatives operate without a fixed schedule, thus enhancing the importance of reliable arrival time (AT) predictions. This study presents an AT prediction system for autonomous shuttles, utilizing separate models for dwell and running time predictions, validated on real-world data from five cities. Alongside established methods such as XGBoost, we explore the benefits of integrating spatial data using graph neural networks (GNN). To accurately handle the case of a shuttle bypassing a stop, we propose a hierarchical model combining a random forest classifier and a GNN. The results for the final AT prediction are promising, showing low errors even when predicting several stops ahead. Yet, no single model emerges as universally superior, and we provide insights into the characteristics of pilot sites that influence the model selection process. Finally, we identify dwell time prediction as the key determinant in overall AT prediction accuracy when autonomous shuttles are deployed in low-traffic areas or under regulatory speed limits. This research provides insights into the current state of autonomous public transport prediction models and paves the way for more data-informed decision-making as the field advances.

Autonomous shuttles (AS) operate in several cities and have shown potential to improve the public transport network. However, there is no car following model that is based on field data and allows decision-makers to assess and plan for AS operations. To fill this gap, this study collected field data from AS, analyzed their driving performance, and suggested changes in the AS trajectory model to improve passenger comfort. A sample was collected with more than 4000 seconds of AS following a conventional car. The sample contained GPS positions from both AS and conventional vehicles. Latitude and longitude positions were used to calculate the speed, acceleration, and jerk of the leader and follower. The data analyses indicated that AS have higher jerk values that may impact the passengers comfort. Several existing models were evaluated, and the researchers concluded that the calibrated ACC model resulted in lower errors for AS spacing and speed. The results of the calibration indicate that the AS has lower peak acceleration and higher deceleration than the parameters that were calibrated for autonomous vehicle models in other research

After announcing its first autonomous shuttle deployment in Portugal on Oct. 10, NAVYA reports a platoon demonstration of two Navya Autonom shuttles beginning Oct. 17 at New York's John F. Kennedy International Airport, a first for a U.S. airport. Navya, a French company specializing in the provision of autonomous mobility systems and related services, has made no secret of its ambition to become the reference player in Level 4 autonomous mobility systems for the transportation of passengers and goods. The Autonom Shuttle, its main development focus, is dedicated to passenger transport. More than 200 units have been sold in 25 countries as of December 31, 2021. It was recently tested in White Bear Lake, Minnoseta, and Inverness, Scotland.

Southwest Research Institute's automated, 14-passenger shuttle is deployed at the company's San Antonio campus. Southwest Research Institute (SwRI) has deployed an autonomous shuttle at its 1,500-acre campus in San Antonio. The shuttle can carry up to 14 passengers for campus tours, while also gathering important information to help researchers better understand autonomous driving. SwRI is a developer of software systems for autonomous vehicles and robotics. Its Ranger system is at work on the shuttle, helping it to autonomously drive unique routes on the campus.

DUBAI: Six years after the Dubai Roads and Transportation Authority laid the roadmap for driverless vehicles by 2030, smart mobility has swept the landscape with intelligent concepts that are changing the region's social infrastructure. The move has already spurred sustainable cities into high gear with smart transportation such as autonomous shuttles, e-bikes and e-buggies set to own the roads. An excellent example of a fully-integrated residential project is Sharjah Sustainable City. This eco-friendly concept is powering a net-zero energy community with energy-efficient villas that promise to offer sustainable living at no extra cost. Developed by Sharjah Investment and Development Authority in partnership with Diamond Developers, the sustainable city will host the best green technology, including solar-powered smart homes, bio-domes for vertical farming, electric vehicle chargers, driverless shuttles and a biogas plant. "The UAE is the first country in the Gulf Cooperation Council to commit to net-zero by 2050; all growth and development must align with that commitment, which means we have to do our bit," Karim El-Jisr, chief sustainability officer, SSC, told Arab News.