The emergence of automated mobility-on-demand (AMoD) services in urban regions has underscored crucial issues concerning the sustainable advancement of urban mobility. In particular, the impact of various AMoD implementation strategies in dense, transit-oriented cities has yet to be investigated in a generalized manner. To address this gap, we quantify the effects of AMoD on trip patterns, congestion, and energy and emissions in a dense, transit-oriented prototype city via high-fidelity simulation. We employ an activity- and agent-based framework, with specific demand and supply considerations for both single and shared AMoD rides. Our findings suggest that, in densely populated, transit-oriented cities such as the Tel Aviv metropolis, AMoD contributes to higher congestion levels and increased passenger vehicle kilometers traveled (VKT). However, when AMoD is integrated with public transit systems or introduced alongside measures to reduce household car ownership, it helps alleviate the VKT impact. Furthermore, these combined approaches effectively counter the negative impact of AMoD on public transit ridership. None of the AMoD strategies analyzed in our study reduce the congestion effects of AMoD and all strategies cannibalize active mobility in dense, transit-oriented cities compared to the base case. Nevertheless, our analysis reveals that a policy leading to decreased car ownership proves to be a more efficient measure in curbing energy consumption and greenhouse gas emissions.

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.

This paper investigates the spatiotemporal patterns and accessibility implications of shared e-scooter use in Tel Aviv, drawing on a complete year (2024) of trip-level data from all licensed providers. Shared micromobility services are often promoted as tools for reducing car dependency and improving urban accessibility, yet their actual usage patterns and equity outcomes remain underexplored, especially outside North America and Western Europe. This study aims to address this gap by integrating over 9 million reconstructed scooter trips with public transport accessibility data, local weather records, and institutional calendar effects. Multivariate regression was applied to quantify temporal and environmental determinants of demand, seasonal-trend decomposition to reveal cyclical usage patterns, and spatial analysis to assess whether scooters extend or reinforce existing mobility hierarchies. Findings indicate that scooter use in Tel Aviv is highly structured, peaking during afternoon hours, dropping during holidays and rain, and reflecting the weekly rhythms of the workweek in Tel Aviv. However, spatial patterns show a strong concentration of usage within already well-connected central areas, with limited activity in low-accessibility zones. These results suggest that shared e-scooters are not currently fulfilling their potential as first- or last-mile connectors; instead, they primarily serve as short-range, intra-core alternatives to walking.

For decades, transportation planning has relied on utilitarian principles, which aim to maximize cumulative benefit by meeting the needs of the “average user.” This approach ignores fundamental differences between population groups and produces uniform solutions that fail to address the diverse needs of women, children, the elderly, and other disadvantaged populations. In response, there are growing calls for a transportation justice paradigm that emphasizes individuals’ ability to access meaningful opportunities according to their characteristics, abilities, and life circumstances. Autonomous Mobility on Demand (AMoD) holds the potential to transform future transportation systems. However, without deliberate planning, they risk replicating existing patterns of inequality for populations whose mobility needs differ from those of the average user. This study applies transportation justice principles to examine how AMoD systems can be designed to meet diverse user needs. Using a combination of an Autoencoder for learning reduced representations and an HDBSCAN clustering algorithm, the analysis identifies distinct travel patterns across socioeconomic groups. These findings reveal significant gaps between population segments, particularly among children and older adults, and demonstrate how AMoD systems could expand access to after-school activities, reduce social isolation among elderly women, and reduce various transportation-related social gaps by improving their ability to reach a wider range of opportunities.

This study examines the uncertain energy impacts of automated on-demand transportation, considering not only vehicle propulsion but also the often-overlooked energy use of onboard and offboard systems required for automation, as well as behavioral changes in mobility patterns that introduce additional complexity. To address these challenges, the research combines agent-based demand modeling with a dynamic fleet controller based on shareability graph principles and cluster analysis, enabling large-scale urban simulations that capture both user behavior and system-level operations. The framework provides a detailed representation of interactions between travel demand, fleet management, and service configuration under different operating conditions. The study further incorporates insights from automated vehicle manufacturers compiled in a European Commission report to derive per-kilometer energy consumption coefficients for both onboard sensing and computing systems and offboard data processing infrastructure. Applied to the Tel Aviv metropolitan area, the simulation evaluates multiple deployment scenarios, including RoboTaxis and automated shuttles, under varying demand levels and service strategies. Results indicate that overall energy demand depends not only on fleet size and travel demand, but also on technological configurations and user preferences. The findings highlight significant hidden energy costs associated with automation, which can offset expected efficiency gains from electrification, and show that prioritizing user convenience over system efficiency limits ride-sharing potential and reduces opportunities for energy savings.

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.