The advent of Urban Air Mobility (UAM) demands a fundamental re-evaluation of the reciprocal relationship between emerging vehicle technologies and the established urban form. Beyond the immediate challenges of engineering optimisation, this study posits that electric vertical takeoff and landing (eVTOL) aircraft design parameters function as primary determinants of metropolitan accessibility, equity, and spatial structure. By developing an integrated infrastructure-cost framework applied to 283,647 building footprints in Shanghai, we systematically trace how seemingly technical choices, specifically wingspan and seating capacity-cascade through the rigid constraints of the built environment to produce profound, often unanticipated societal outcomes. The empirical analysis reveals a decisive "spatial filtering" mechanism: as vehicle wingspan increases from 6 to 16 m, feasible vertiport sites decline by 75.7 percent, with aggregate network capacity collapsing by 93.0 percent. Crucially, this attrition is disproportionately concentrated in dense, high-value central districts, effectively forcing larger aircraft to the urban periphery. These findings challenge the prevailing technology-centric discourse, demonstrating that the accumulated legacy of urban morphology-building codes and land use patterns unrelated to aviation-acts as a binding constraint on technological deployment. Consequently, the interaction between vehicle and infrastructure dictates the economic geography of the system. We find that optimised, compact Multirotor configurations achieve costs of $0.063 per passenger-kilometre, drastically undercutting larger winged architectures not merely due to energy efficiency, but through superior infrastructure compatibility. This implies that design decisions made upstream in the manufacturing process carry embedded assumptions about who will benefit from aerial mobility: compact designs foster accessible, city-centre integration, while larger footprints risk entrenching UAM as a premium, exclusionary service restricted to peripheral corridors. Ultimately, this study illustrates that new mobility modes do not simply reshape the city; they are profoundly shaped by it. We argue that vehicle design cannot be treated in isolation from the urban systems it serves. For planners and policymakers, this underscores that preserving optionality for equitable transportation requires foresight into how the physical constraints of the city interact with the dimensions of the machine, demanding a holistic approach to designing our urban futures.

Urban air mobility (UAM) demands reliable sensing and communication to ensure safe and efficient electric vertical take-off and landing (eVTOL) operations in complex urban environments. This paper proposes a multitier tower base station (TBS) and tethered unmanned aerial vehicle (TUAV)-assisted integrated sensing and communication (ISAC) framework for the eVTOL approach and landing, where the rigid shapes of eVTOLs are represented by the millimeter wave (mmWave) radar point clouds rather than simplifying them as a single point, which is closer to the real world. Then, an integrated radar–communication fair optimization problem is formulated to jointly maximize radar and channel capacities while satisfying tether, safety, and terminal constraints. To address the randomness of radar point clouds, a point-cloud-aware deep reinforcement learning (PCDRL) method is developed to solve the above optimization problem. The proposed framework extracts radar point clouds features and learns adaptive TUAV 3D trajectory control and dynamic power ratio allocation across multiple eVTOLs. Simulation results demonstrate that PCDRL improves both radar and communication performance, achieving challenging performance among all the benchmarks.

Abstract The low-altitude economy is expanding urban mobility and service systems from ground-based networks to three-dimensional urban space. However, its large-scale deployment depends not only on aircraft technologies, but also on reliable operational infrastructure for communication, navigation, surveillance, meteorological sensing, computing, and safety supervision. This paper argues that smart infrastructure originally developed for connected and automated vehicles can provide a reusable foundation for low-altitude operations. Because low-altitude public routes are likely to follow existing urban corridors such as roads, railways, rivers, and utility corridors, there is strong spatial overlap with ground-based intelligent transportation infrastructure. By extending these capabilities upward, cities can reduce duplicate investment, improve infrastructure utilization, and support safe, scalable, and city-level air-ground operation management.

As operational volumes outpace human review capacity, low-altitude airspace authorization increasingly requires automated decision support. Yet no benchmark exists to evaluate whether artificial intelligence (AI) systems can reliably approve, conditionally approve, or reject flight requests under operational complexity. We construct the Low-Altitude Economy Benchmark (LAE-Bench), a suite of 368 parameterized test cases derived from 49 scenarios grounded in 23 real operational cases from China’s low-altitude transportation network, and validate it against both China’s Interim Regulations on the Flight Management of Unmanned Aerial Vehicles (UAVs) and the US Federal Aviation Administration (FAA) Part 107. A promptonly large language model (LLM) achieves 100% accuracy on 180 standardized civil aviation cases but drops to 50.39% on contested low-altitude scenarios involving regulatory ambiguity, ethical trade-offs, and adversarial manipulation. Retrieval-augmented generation (RAG) reduces errors by 78%, raising overall accuracy to 88.98%, and shifts the residual error profile from bidirectional to predominantly conservative: 75.0% of remaining failures are over-rejections, while over-permissive errors fall from 22 to 2 cases. The 28 residual errors cluster in five failure modes: conditional reasoning failure, solution-generation deficit, prompt injection vulnerability, knowledge conflict misresolution, and epistemic uncertainty miscalibration. These errors are better explained by reasoning failures than by jurisdiction-specific knowledge gaps. The results support a tiered governance logic: deterministic constraint checks can be automated; contested cases are better treated as AI-assisted recommendations with explicit uncertainty flags and human approval before execution; adversarial or high-ambiguity cases remain under human authority. The same failure categories appear in both Chinese- and US-tagged scenarios, although a more balanced cross-jurisdiction benchmark is needed to test that pattern rigorously.

Connected and autonomous vehicles (CAVs) have been deployed rapidly at unstructured intersections. To prevent frequent stops and ensure safety, CAVs should pass through the intersection within recommended departure time windows while adhering to specific motion constraints on terrains with irregular boundaries and slopes. It leads to a multi-vehicle trajectory planning (MVTP) problem at intersection areas, for which a mixed-integer nonlinear programming (MINLP) model with non-differentiable multiple if-else expressions is applicable but difficult to solve efficiently by existing optimizers. To this end, this study proposes a centralized-distributed computing framework to tackle this problem in an efficient manner. Specifically, at the centralized level, an improved conflict-based search (CBS) and hybrid A* algorithm is used to generate coarse trajectories satisfying departure time windows constraints whereas handling unknown if-else expressions. At the distributed level, each CAV performs a simple NLP-based trajectory optimization, with collision-avoidance ensured through linear corridor constraints. Experimental validation and comparisons with benchmarks demonstrate that the effectiveness and efficiency of the proposed framework.

Organizations increasingly rely on sequential experimentation to improve decision-making. While the multi-armed bandit literature has developed algorithms with strong asymptotic regret guarantees, many practical applications operate over finite and externally imposed horizons. Motivated by the finite-horizon setting, we develop a class of regularized greedy algorithms for multi-armed Bernoulli bandits. We derive the first finite-horizon regret envelopes for regularized greedy bandits, showing that finite-horizon regret decomposes into transient exploration costs and a suboptimal convergence term that decays exponentially with the regularization strength. This characterization yields principled calibration rules for the regularization parameters and, as a limiting case, sharper regret guarantees for the classical greedy policy. Across extensive numerical experiments, calibrated regularized greedy policies consistently match or outperform state-of-the-art algorithms. These results suggest that regularized greedy policies can provide an effective approach for finite-horizon bandit problems.

The scarcity of charging facilities remains one of the primary obstacles to the advancement of electric vehicles. Particularly, in ride-sourcing markets, drivers of ride-sourcing electric vehicles (REVs) often spend excessive time searching for an unoccupied public charging pile, leading to reduced customer service efficiency and lower long-term driver profits. To mitigate the inefficiencies caused by searching for limited public charging piles, this study introduces a charging-sharing scheme embedded within the ride-sourcing platform. In this scheme, the ride-sourcing platform operator rents idle private charging piles, located in residences, communities, companies, and industrial parks, and makes them available for reservation through the online platform. Under this integrated system, REV drivers can either reserve shared private charging piles via the online platform or search for available public charging piles. We model the complex interactions between ride-sourcing services and the charging pile choice as a joint ride-sourcing market equilibrium and charging choice equilibrium under the steady state. We prove the existence and uniqueness of the joint equilibrium and analytically investigate the impact of the charging-sharing pricing strategy on both charging choices and market outcomes. We identify conditions under which system performance, measured by the number of REVs in service, customer demand, and customer waiting time, is optimized. Furthermore, we determine the optimal pricing strategy to maximize either platform profit or total social welfare. Notably, profit-maximizing strategies can lead to a Win-Win-Win-Win outcome for drivers, customers, charging pile sharers, and the platform operator. Numerical results demonstrate that when public charging infrastructure is limited, intense competition for charging piles significantly reduces service capacity and customer satisfaction. Introducing the charging-sharing scheme substantially increases the number of REVs in service, boosts customer demand, and shortens customer waiting times. Moreover, pricing strategies play a crucial role in influencing equilibrium outcomes, enhancing platform profitability, and improving overall social welfare.

In large-scale highway networks, fast-charging infrastructure is often constrained by land availability and grid capacity, while service congestion leads to substantial waiting time. Battery swapping offers faster service and reduced grid stress, motivating the coordinated planning of charging and swapping facilities. This study develops a bilevel location–capacity planning model for fast-charging and battery-swapping infrastructure in large-scale highway networks. The upper level determines facility locations and capacities to minimize total system cost, while the lower level captures driver route choice behavior under detour tolerance and energy-feasibility constraints. Queueing-based service reliability is explicitly incorporated to represent congestion and waiting-time effects at charging stations. The bilevel model is reformulated into a single-level mixed-integer linear program via Lagrange duality, and an exact hierarchical multi-subproblem Benders decomposition algorithm is proposed to ensure computational tractability for large networks. Numerical experiments demonstrate that coordinated charging–swapping deployment, moderate detour flexibility, and queue-aware capacity sizing significantly reduce redundant investment and improve system efficiency. Overall, the proposed framework provides a behaviorally grounded and scalable approach for designing cost-effective energy-replenishment networks.

Advanced air mobility (AAM) is expected to introduce increasingly heterogeneous low-altitude operations, and mature AAM deployment may create locally high-density traffic around capacity-constrained vertiports. This paper considers mixed operational scenarios in which unmanned aerial vehicles (UAVs) and electric vertical takeoff and landing (eVTOL) aircraft share the same vertiport and surrounding terminal airspace. In such environments, eVTOL operations typically follow air traffic management (ATM) procedures and are strategically deconflicted in advance, resulting in schedules that are largely inflexible. By contrast, certain UAV missions, such as time-critical medical deliveries following intercity eVTOL transport, require immediate departure and cannot wait for global replanning. Safely allowing emergency UAVs to egress from locally dense or capacity-constrained terminal airspace without disrupting pre-planned eVTOL operations therefore poses significant operational challenges. To address this problem, we develop an Asynchronous Proximal Policy Optimization (APPO) based reinforcement learning approach for emergency UAV egress that explicitly encodes separation safety and egress efficiency. A distributed policy-training framework based on Ray RLlib is developed, and the simulation environment incorporates the AirLib flight-control model from AirSim to represent low-level UAV maneuver responses during speed and heading command execution. Simulation results demonstrate that, under locally dense terminal traffic conditions, the proposed approach demonstrates stronger overall robustness than APF, VO, RRT, Modified CL-RRT, and MCTS-UCT in terms of safe-egress performance, separation maintenance, and online computational efficiency.

Urban air mobility (UAM) represents a transformative shift in urban transportation. However, its operational safety within low-altitude urban environments remains a paramount concern that may undermine public acceptance and hinder practical implementation. To improve system resilience and safety, this paper proposes a joint planning model for vertiports and backup landing sites (BLSs) that explicitly incorporates safety considerations into UAM network design. Specifically, the model simultaneously optimizes vertiport and BLS locations, flight route establishment, and BLS assignments, capturing the interdependencies between strategic network planning and contingency landing scenarios. To evaluate network-level safety performance, we develop a generalized safety cost framework based on a set of generated contingency landing events (CLEs) along UAM flight routes. The framework quantifies multiple dimensions of safety impact, including diversion time, low-altitude airspace conflicts, and disruptions to ground passengers. The overall planning objective is to minimize the total network safety cost while satisfying the UAM demand coverage requirements and service constraints. A comprehensive case study based on real-world travel demand data from Beijing is conducted to demonstrate the applicability of the proposed framework. Sensitivity analysis is also performed to explore the impact of several key planning parameters on system performance. The findings highlight the necessity of incorporating safety as a primary consideration in early-stage UAM network planning and provide practical insights for designing safe UAM systems.

Urban aerial mobility (UAM) presents a high-speed travel solution, offering new perspectives on addressing congestion. Existing demand analyses for UAM do not consider its reciprocal feedback relationship with the inherent transportation system. This paper, grounded in the four-step transportation demand modeling theory, examines the impact of UAM on the transportation system and the quality of urban commuting in Shenzhen during peak hours. Our findings indicate: (1) UAM accounts for 5.24

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A well-structured urban park system (UPS) is crucial for optimizing urban spatial layout and improving the quality of the human living environment. In response to the tendency of current planning to prioritize quantitative indicators while overlooking the relational structure arising from the collective spatial configuration of parks, this study introduces Social Network Analysis (SNA) to evaluate the spatial structure of Shanghai's park system by constructing a service-coverage overlap network. The findings reveal the following: (1) Parks with high degree centrality are concentrated in high-density urban core areas due to service overlap, whereas large suburban parks with high betweenness centrality function as critical bridging hubs, reflecting a polycentric structure. (2) There is a discernible discrepancy between these emergent network tiers and the statutory park hierarchy, highlighting a tension between bottom-up spatial patterns and top-down planning frameworks. (3) Stability simulations indicate a dual character of the system, where the network topology is vulnerable to attacks yet functionally resilient to failures due to spatial redundancy, suggesting that a decline in service quality may precede the loss of basic accessibility. This study demonstrates the value of SNA in diagnosing park system structure, identifying key nodes, and assessing system resilience. The insights advocate for planning approaches that transcend rigid hierarchical frameworks, integrate the actual functional roles of parks, and protect structural hubs, thereby enhancing systemic resilience and promoting equitable service provision.

Driven by rapid advancements in electric vertical take-off and landing (eVTOL) technology, urban air mobility (UAM) has attracted unprecedented attention worldwide, with governments, industries, and researchers exploring its potential to revolutionize urban transportation. In this paper, we conduct a comprehensive review of key research problems in UAM to establish a foundational knowledge framework and provide insights for researchers, policymakers, and industry stakeholders. Specifically, we examine UAM-related studies and reports from the perspectives of planning, operations, and management, including topics such as infrastructure development, airspace management, and service optimization. Additionally, the potential societal impact and public acceptance of UAM are explored to provide a balanced view of opportunities and challenges in this emerging field. The application of UAM in several representative scenarios is also analyzed to examine the operational feasibility of integrating this new mobility solution into modern urban transportation networks. Based on our review findings, we identify a series of challenges and open questions that need to be adequately addressed for future UAM commercialization. Finally, the paper concludes with a discussion of potential research directions aimed at designing a more reliable and scalable UAM network.

To support early urban air mobility (UAM) deployment, this study develops an integrated planning framework for vertiport-centered network design. The framework is formulated as a mixed-integer linear program (MILP) that co-optimizes vertiport siting and capacity, direct and transfer services, fleet scheduling, and network flow balance, while endogenizing discrete eVTOL performance profiles and coupling vehicle-level energy feasibility with network routing and service decisions. A case study of Guangzhou shows that direct and transfer services play complementary roles in shaping network efficiency. Direct services dominate high-demand corridors, whereas transfer itineraries (i) extend connectivity and enable peripheral-to-peripheral accessibility and (ii) on selected long-distance corridors, yield efficiency gains by splitting long constrained legs to avoid endurance-induced cruise-speed reductions, thereby increasing effective travel speed with limited geometric detours. The optimized network is self-balancing with no empty repositioning flights, as passenger-carrying multi-leg itineraries implicitly absorb the balancing movements that would otherwise require deadheading. Despite system-level door-to-door time advantages, first-and last-mile access and in-vertiport processes remain the dominant bottlenecks, with effective flight time contributing approximately 10-30% of door-to-door time. Deployment feasibility is highly sensitive to operating-altitude standards: raising the safety altitude from 150 m to 300 m sharply reduces route feasibility under baseline battery technology, whereas a 25% increase in specific energy preserves 43.7% of the baseline route availability at 300 m while maintaining high cruise speeds. These findings underscore the value of system-level co-optimization of infrastructure, operations, and vehicle performance, and the necessity of evaluating deployment policies in light of plausible technology trajectories, especially near-term improvements in energy storage.

Electric service vehicles (ESVs), such as mobile chargers and drone-based service units, are becoming an important operational resource for on-demand service systems. Unlike conventional spatial servers, ESV operations are shaped by battery limits and recharging needs, which affect dispatch feasibility and spatial deployment decisions. We develop an energy-constrained hypercube spatial queueing model that embeds battery-state dynamics into the classical hypercube framework and uses a semi-Markov representation to estimate steady-state performance. We then formulate a joint location–zoning problem for station placement and service zone design. The resulting large-scale mixed-integer nonlinear program admits a set partitioning reformulation whose column coefficients are not available in closed form. We therefore develop a Branch-Price-and-Evaluation framework for set partitioning problems with externally computable column coefficients: upper-bounding surrogates guide pricing, and iterative exact evaluation updates the coefficients of active columns. Computational results show that explicit energy modeling significantly reduces false service promises and yields more credible planning decisions. They also reveal a load-dependent reversal in zoning: pooling is preferable under light demand, whereas tighter zoning becomes more profitable as demand increases. Over the tested range, profitability is driven more by zoning than by battery improvement, suggesting that managers should get service zone design right before investing in battery upgrades; this caution is reinforced by the counterintuitive finding that larger batteries may delay replenishment and reduce fleet readiness under sparse demand. These findings show that energy feasibility is not merely a matter of battery-capacity expansion, but a design dimension that shapes service-zone configuration.

The market for remanufactured products made from marine plastic waste is expanding rapidly, but the recycling rate of this waste remains strikingly low. This disconnect forces conventional plastic recycling firms to make a consequential strategic choice: enter the marine plastic recycling supply segment by expanding to build market power or enter by competing as a specialized supplier. To examine this trade-off, this paper develops a two-period game-theoretic model that contrasts entry strategies and performance under monopolistic and competitive market structures. We derive and compare equilibrium pricing, quantities, and profits for the relevant supply chain participants in both settings and then characterize the conditions under which one entry mode dominates the other. The results indicate that neither the preferred entry strategy nor the profitability that follows is driven by a single parameter. Instead, outcomes are shaped by the joint effects of consumer tastes, remanufacturing costs, and the scale of capacity investment cost required for entry. When consumers show a stronger preference for conventional remanufactured products, a supplier pursuing monopolistic expansion can earn higher profits by offering a more flexible product portfolio. By contrast, when the cost of remanufacturing marine plastics and the associated capacity investment cost are relatively low, the environment favors a specialized, competitively oriented entry strategy. Profit allocation within the supply chain is also closely tied to remanufacturer costs: as these costs fall, suppliers are able to appropriate a larger share of total profits. Overall, the analysis provides a theoretical basis for entry decisions in the emerging marine plastic recycling industry and offers actionable guidance for firms facing different demand and cost conditions across market structures.

Urban air mobility (UAM), enabled by electric vertical take-off and landing (eVTOL) services, provides a potential alternative to congested ground transportation. This study develops a doubly dynamical modeling framework to investigate the interactions among eVTOL deployment, ground traffic dynamics, and travelers' behavioral adaptation in a multimodal transportation network. At the within-day level, ground traffic is represented using an aggregated regional approach, where the macroscopic fundamental diagram (MFD) characterizes the relationship between vehicle accumulation and traffic speed. Time-varying demand and inter-regional vehicle movements determine route travel times and experienced generalized costs. At the day-to-day level, travelers update perceived costs based on previous travel experience and adapt their mode and route choices through a nested Logit model, forming a feedback loop between traffic evolution and travel behavior. A multi-stage adaptive congestion pricing mechanism is further introduced, in which tolls are updated according to traffic conditions observed in the preceding stage and incorporated into both predicted and experienced route costs. Numerical experiments compare scenarios with and without eVTOL services and evaluate alternative congestion pricing strategies. The results show that eVTOL services can redistribute travel demand across modes and alleviate congestion in highly congested regions, while day-to-day learning drives travel choices toward stable patterns. Adaptive congestion pricing provides a more responsive mechanism for managing dynamically evolving congestion than static pricing. Sensitivity analysis further reveals the trade-off among eVTOL fare, passenger demand, and operator revenue. The proposed framework provides a quantitative basis for evaluating the coordinated operation and management of future low-altitude and ground transportation systems.

Electric microtransit system-equipped with platooning capabilities via advanced automation and communication technology-offers opportunities to tackle the resource allocation challenges of traditional bus system in face of fluctuating passenger demand. In this paper, we introduce a novel vehicle scheduling framework in which microtransit units can be dynamically coupled and detached to accommodate imbalanced trip demands through flexible capacity adjustment. We propose the electric microtransit scheduling with platooning problem (EMSP), which jointly optimizes trip chains and charging plans for individual microtransit units, as well as platoon formation and departure time for each trip, while explicitly accounting for traffic state stochasticity. The problem is formulated as a two-stage stochastic program that optimizes vehicle assignment, trip sequencing, and charging planning in the first stage, and incorporates departure shifting and charging-duration adjustment as recourse decisions in response to travel time and energy consumption uncertainty. An exact solution methodology based on a combination of branch-and-price and L-shaped algorithms is developed to solve the problem, with additional acceleration strategies achieved through the integration of both decomposition techniques. Computational experiments demonstrate the scalability and efficiency of the proposed approach, as well as its robustness relative to deterministic scheduling. Using real-world data from Wuxi, the results indicate that the proposed EMSP framework with flexible operating modes significantly reduces total system cost and improves resource utilization compared to conventional transit, particularly under low-demand scenarios. Moreover, higher-capacity microtransit vehicles-provided that their capacity is not excessively redundant relative to demand-tend to achieve lower total cost due to economies of scale.