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Abstract Maintaining airport runways is crucial for safety and efficiency, yet traditional monitoring relies on manual inspections, prone to time consumption and inaccuracy. This study pioneers the utilization of low‐cost dashcam imagery for the detection and geolocation of airport runway pavement distresses, employing novel deep‐learning frameworks. A significant contribution of our work is the creation of the first public dataset specifically designed for this purpose, addressing a critical gap in the field. This dataset, enriched with diverse distress types under various environmental conditions, enables the development of an automated, cost‐effective method that substantially enhances airport maintenance operations. Leveraging low‐cost dashcam technology in this unique scenario, our approach demonstrates remarkable potential in improving the efficiency and safety of airport runway inspections, offering a scalable solution for infrastructure management. Our findings underscore the benefits of integrating advanced imaging and artificial intelligence technologies, paving the way for advancements in airport maintenance practices. 

An important thread in the study of data-stream algorithms focuses on settings where stream items are active only for a limited time. We introduce a new expiration model, where each item arrives with its own expiration time. The special case where items expire in the order that they arrive, which we call consistent expirations, contains the classical sliding-window model of Datar, Gionis, Indyk, and Motwani [SICOMP 2002] and its timestamp-based variant of Braverman and Ostrovsky [FOCS 2007]. Our first set of results presents algorithms (in the expiration streaming model) for several fundamental problems, including approximate counting, uniform sampling, and weighted sam- pling by efficiently tracking active items without explicitly storing them all. Naturally, these algorithms have many immediate applications to other problems. Our second and main set of results designs algorithms (in the expiration streaming model) for the diameter and k-center problems, where items are points in a metric space. Our results significantly extend those known for the special case of sliding-window streams by Cohen-Addad, Schwiegelshohn, and Sohler [ICALP 2016], including also a strictly better approximation factor for the diameter in the important special case of high-dimensional Euclidean space. We develop new decomposition and coordination techniques along with a geometric dominance framework, to filter out redundant points based on both temporal and spatial proximity. 

Reliable paratransit services are essential for urban mobility, particularly for individuals with disabilities who depend on demand-responsive transportation. However, service inefficiencies in urban paratransit systems, such as travel time variability, congestion impacts, and scheduling constraints, continue to pose significant challenges. This study introduces a multitier analytical framework that integrates spatial–temporal modeling, machine learning–based outlier detection, and causal inference to systematically evaluate the reliability of paratransit services in an urban context. Leveraging trip transaction data from a metropolitan paratransit system, we develop the paratransit efficiency index (PEI) to assess travel time reliability at both system-wide and individual trip levels. We then analyze PEI's spatial–temporal variability using geographically and temporally weighted regression and identify outlier trips with high PEI using XGBoost to pinpoint service unreliability and systemic inefficiencies. Later, we utilize causal inference techniques to show that peak-hour pick-ups causally contribute to travel time inefficiency (a 19.4% relative increase in outlier probability), whereas subscription-based bookings causally improve service consistency (a 33.9% relative decrease). The findings provide actionable insights for urban planners and transit agencies to optimize scheduling, mitigate congestion effects, and explore innovative strategies such as integrating transportation network companies for high-cost or unreliable trips. By addressing critical urban transportation equity issues, this study offers data-driven solutions to enhance the efficiency and resilience of paratransit services in growing metropolitan areas. 

Paratransit, a demand-responsive transit mode serving passengers with mobility challenges, is increasingly electrified to enhance urban transportation sustainability. However, high investments required for dedicated charging infrastructure and the scarcity of public charging resources remain significant hurdles to large-scale deployment. This study investigates a shared charging scheme that integrates paratransit electric vehicles (EVs) into existing electric bus (EB) charging networks. A fuzzy multi-objective optimization framework is proposed to identify optimal charging co-hub locations and EV assignments by balancing supply–demand dynamics. The framework incorporates two-step floating catchment area (2SFCA) and inverted 2SFCA (i2SFCA) methods to formulate objectives and constraints for EB and paratransit systems, respectively. Through fuzzy programming, trade-offs among supply–demand dynamics are resolved, yielding efficient shared-charging plans. The framework is validated with Utah Transit Authority data, demonstrating improved charging accessibility and operational efficiency while offering actionable insights for transit agencies in planning shared charging schemes among various public transport modes. 

Electrification of vehicle fleets has advanced significantly in recent years to achieve net-zero greenhouse gas (GHG) emissions. As a cost-effective strategy, shared charging facilities are increasingly used by public and private sectors. For example, the unoccupied time of a bus charging station can be leveraged to charge other electric vehicles (EVs). This shared usage model presents both opportunities and challenges for organizations considering transitions to electrified mobility. It is especially difficult when considering the variability in daily fleet operations and the availability of charging infrastructures. This paper presents a bi-objective optimization model designed to strategically guide the replacement of vehicle fleets with EV. The model aligns the spatial- temporal dynamics of vehicle routes with the availability of shared charging facilities. It is particularly relevant for organizations managing vehicle fleets that are considering a strategic transition to EVs, with the goals of minimizing GHG emissions from fuel consumption and vehicle idling, and reducing operational delays (e.g. detour and charging time for the EV fleet). We applied this model to the University of Utah campus fleet, utilizing shared charging facilities operated by the Utah Transit Authority. The results demonstrate effective strategies for replacing vehicles with varied operational characteristics, offering detailed plans and schedules that balance GHG emission reductions with operational efficiency. Additionally, we conducted a sensitivity analysis to assess the effects of different battery sizes, station disruptions, and traffic delays on the model’s outcomes and a feasibility analysis to prioritize the replacement of high-utility vehicles. Our research provides a foundation for fleet agencies to develop strategic EV replacement plans that consider multiple goals and leverage shared charging infrastructure, ultimately leading to optimized charging facility utilization and reduced maintenance costs. These strategies support more efficient, reliable, and sustainable operations in urban fleet systems. 

Linear probing-based hash tables offer high data locality and are considered among the fastest in real-world applications. However, they come with an inherent tradeoff between space efficiency and speed, i.e. when the hash table approaches full capacity, its performance tends to decline considerably due to an effect known asprimary clustering.As a result they are only used at low load factors. Tombstones(markers for deleted elements) can help mitigate the effect of primary clustering in linear probing hash tables. However, tombstones require periodic redistribution, which, in turn, requires a complete halt of regular operations. This makes linear probing not suitable in practical applications where periodic halts are unacceptable. In this paper, we present a solution to forestall primary clustering in linear probing hash tables, ensuring high data locality and consistent performance even at high load factors. Our approach redistributes tombstones within small windows, deamortizing the cost of mitigating primary clustering and eliminating the need for periodic halts. We provide theoretical guarantees that our deamortization method is asymptotically optimal in efficiency and cost. We also design an efficient implementation within dominant linear-probing hash tables and show performance improvements. We introduce Zombie hashing in two variants: ordered (compact) and unordered (vectorized) linear probing hash tables. Both variants achieve consistent, high throughput and lowest variance in operation latency compared to other state-of-the-art hash tables across numerous churn cycles, while maintaining 95% space efficiency without downtime. Our results show that Zombie hashing overcomes the limitations of linear probing while preserving high data locality.