Search Results (141)

Public health emergencies require spatial crowdsourcing platforms to finish urgent tasks while limiting unnecessary movement across regions. Most online task assignment studies focus on profit, travel distance, latency, task coverage, or service quality. However, isolation sensitive scenarios need a different assignment goal. In such scenarios, regional crossings should be directly controlled during worker–task matching. This paper studies an isolation sensitive online task assignment problem in spatial crowdsourcing. The service space is modeled as a regional adjacency graph. The matching objective combines cross-region movement cost, an urgency reward for delayed task completion, and a dummy no-assignment cost for carry-over decisions. To handle dynamic arrivals, a time-sliced online process is used. Unfinished tasks are carried over to later time slots, and the priority of each carried-over task increases with waiting time. Based on this framework, we design two algorithms. OnlineKM serves as the basic priority-aware online matching algorithm. OnlineKM builds a matching problem in each time slot and applies KM-based partial matching with the information currently available. OnlineARC further uses δ-balanced adaptive regional coarsening. OnlineARC merges adjacent regions according to recent supply–demand balance before matching. This step adjusts the regional granularity used for movement cost evaluation and helps keep assignments close to local regions when regional merging is suitable. Experiments are conducted using a real-world task locations dataset from a 2022 COVID-19-related scenario in Changchun, with simulated worker availability and online arrivals. The results show that the proposed methods usually reduce the combined assignment objective value under the tested settings. The service quality and movement control metrics show that OnlineARC reduces the cross-region assignment ratio and average hop distance while maintaining a high task completion rate under the representative setting. OnlineKM improves running efficiency through time-sliced matching, while OnlineARC provides a trade-off between adaptive coarsening cost and locality-aware movement cost evaluation. These results suggest that adaptive regional coarsening can serve as a practical heuristic for locality-aware online task assignment in isolation sensitive spatial crowdsourcing under suitable worker–task distributions. Full article

This paper investigates a novel variant of the two-echelon vehicle routing problem (2E-VRP) within the context of modern e-commerce logistics. The model integrates time windows, occasional trucks, occasional drivers, heterogeneous regular vehicles, and two increasingly relevant types of intermediate facilities: transshipment nodes and parcel lockers. Goods are transported from a first-echelon depot to intermediate facilities via occasional trucks and heterogeneous regular trucks, then delivered to customers through occasional drivers and heterogeneous regular riders at the second echelon, or retrieved by customers from designated parcel lockers. Given significant disparities in service scope, vehicle attributes, delivery rules, and cost structures between the two echelons, the crowdsourcing model for the first echelon is redesigned, and new intermediate facilities are incorporated to expand application scenarios and further enhance the operational advantages of the crowdsourcing mode. The objective aims to minimize transportation costs and minimize customer dissatisfaction costs. This study constructed a two-echelon vehicle routing optimization model under the crowdsourcing mode with time windows and intermediate facilities and developed an improved multi-objective sparrow algorithm (IMOSSA) to solve it. Algorithm comparisons and extensive case studies validate the algorithm’s effectiveness and superiority. Scenario analysis investigates the impact of crowdsourced vehicles on routing decisions, experimentally demonstrates the crowdsourcing model’s advantages, and derives practical managerial insights. Full article

Dynamic Spectrum Access (DSA) has emerged as a viable solution to address spectrum scarcity in shared-spectrum networks. In response, the FCC established the Citizens Broadband Radio Service (CBRS) to manage and facilitate shared use of the federal and non-federal spectrum in a three-tiered access and authorization framework. However, due to the open nature of spectrum access and the usually limited coverage of the monitoring infrastructure, enforcing access rights in a shared-spectrum network becomes a daunting challenge. In this paper, we stipulate the use of crowdsourcing as a viable approach to engaging volunteers in spectrum monitoring in order to enforce spectrum access rights robustly and reliably. The success of this approach, however, hinges strongly on ensuring that spectrum access enforcement is carried out by reliable and trustworthy volunteers within the monitored area. To this end, a hybrid spectrum monitoring framework is proposed, which relies on opportunistically recruiting volunteers to augment the otherwise limited infrastructure of trusted devices. Although a volunteer’s participation has the potential to enhance monitoring significantly, their mobility may become problematic in ensuring reliable coverage of the monitored spectrum area. To ensure continued monitoring, inspite of volunteer mobility, deep learning-based models are used to predict the likelihood that a volunteer will be available within the monitoring area. Three models, namely LSTM, GRU, and Transformer, are explored to assess their feasibility and viability to predict a volunteer’s availability likelihood over an extended time interval, in a given spectrum monitoring area. Recurrent Neural Networks (RNNs) such as GRU and LSTM are effective for tasks involving sequential data, where both spatial and temporal patterns matter, which is the focus of volunteer availability prediction in spectrum monitoring. Transformers, on the other hand, excel at handling long range dependencies and contextual understanding. Furthermore, their parallel processing capabilities allows faster training and inference compared to RNN-based models like GRU and LSTM. A simulation-based study is developed to assess the performance of these models, and carry out a comparative analysis of their ability to predict volunteers’ availability to monitor the spectrum reliably. To this end, a real-world trace dataset of volunteers’ location, collected over five years, is used. The simulation results show that the three models achieve high prediction accuracy of volunteers’ availability, ranging from 0.82 to 0.92. The results also show that a GRU-based model outperforms LSTM and Transformer-based models, in terms of accuracy, Root Mean Square Error (RMSE), geodesic distance, and execution time. Full article

Smart cities require effective disaster management (like flooding, solar storms, sandstorms, or hurricanes), as it directly impacts people’s lives. The key challenges of disaster management are timely detection and effective notification during the crisis. This research presents a smart multi-layer framework for notification classification and management before and during flooding disasters. The framework includes an early detection module as the main phase in the alerting process. This step depends on an Ensemble Learning (EL) model based on a triad of the three best selected models (Deep Learning (DL), Random Forest (RF), and K-nearest Neighbor (KNN)) to analyze data collected continuously from the Internet of Things (IoT) layer. In the boosting phase, the framework utilizes Large Language Models (LLMs) with DL to analyze social textual crowdsourcing data. The results will enable the framework to identify the most affected areas during a flood. The framework adds a fog computing layer alongside a cloud layer to enable instantaneous processing of user responses and generate specialized alerts based on contextual factors such as location, time, risk level, alert type, and user characteristics. Through testing and implementation, the proposed algorithms demonstrated an accuracy rate of over 98% in detecting threats using a dataset of real, collected weather and flooding data. Additionally, the framework proposes a centralized control panel and a design of a smartphone application that offers essential services and facilitates communication among managed civil defense teams, citizens, and volunteers. Full article

In indoor positioning systems, it is common to use existing AP deployments within buildings to build a fingerprint database, providing positioning information during the online phase. However, AP layouts inside buildings often contain a large number of redundant APs, which leads to the improvement in positioning accuracy leveling off as the number of redundant APs increases, while also increasing the computational load of indoor positioning services. To address this problem, the thesis proposes a method for calculating the AP location discrimination capability and combines the location discrimination capability with coverage to eliminate redundant APs. Experiments conducted in real indoor scenarios, as well as on the Crowdsourced dataset and the SODIndoorLoc dataset, validate the results. The results show that the redundant AP removing strategy ensures that the average positioning accuracy fluctuates by no more than 5% compared to the unfiltered case, while significantly reducing the number of APs in the fingerprint database—by 64.43%, 72.78%, and 59.62%, respectively. In the position estimation phase, this paper uses affinity propagation clustering for coarse positioning and combines Bayesian methods for fine positioning. Compared with GMM, K-Means, and the pointwise algorithm, the average positioning error of the proposed method is reduced by 11% to 39%. Full article

Analysis of a crowdsourcing Markovian queue with phase-type service is considered in this paper. In this model, a customer not only receives service but also assists in delivery. In other words, in a retail environment, while some customers shop in-store, others place orders online or by phone and require home delivery. Store management can utilize online customers as couriers to complete these deliveries. However, because not every customer may agree to take part, a probabilistic element is included to capture the chances of their participation. The model also incorporates imperfect service, reflecting cases where deliveries may fail or require rework, and working breakdowns, representing partial disruptions in service capacity rather than complete stoppages. To analyze the system under steady-state conditions, matrix-analytic methods are applied. Numerical examples illustrate the significant benefits of incorporating these dynamics into traditional queueing models. Full article

Background: Over the past decade, the growth of ecommerce and omnichannel order fulfillment has led to a spike in last-mile delivery services. Last-mile delivery being the most expensive portion of the supply chain has resulted in process improvement initiatives by industry and academia targeting lower operational costs. Methods: In this study, we use simulation to account for the daily randomness regarding order quantities with missed deliveries being rolled over to the next period and attrition of the capacities used to meet the demand for each period. Further, to alleviate the impact on operations due to attrition, we consider the use of automation as a replacement for permanent capacity. Results: From the simulation results, we observe that the negative operational impact of employee turnover can be overcome with a combination of delivery robots and crowdsourcing with a payback period as short as 1.55 years. Conclusions: Optimal resource allocation is further refined by the use of simulation. The use of advanced automation such as robots seems to be a viable option for businesses to lower operational costs for some scenarios. Full article

Today’s rapidly increasing number and performance of Remotely Piloted Aircraft Systems (RPASs) and sensors allows for an innovative approach in monitoring, mitigating, and responding to natural disasters and risks. At present, there are 100s of different RPAS platforms and smaller and more affordable payload sensors. As natural disasters pose ever increasing risks to society and the environment, it is imperative that these RPASs are utilized effectively. In order to exploit these advances, this study presents the development and validation of a Natural Disaster Information System (NDIS), a geospatial decision-support framework for RPAS-based natural hazard missions. The system integrates a global geohazard database with specifications of geophysical sensors and RPAS platforms to automate mission planning in a generalized form. NDIS v1.0 uses decision tree algorithms to select suitable sensors and platforms based on hazard type, distance to infrastructure, and survey feasibility. NDIS v2.0 introduces a Random Forest method and a Critical Path Method (CPM) to further optimize task sequencing and mission timing. The latest version, NDIS v3.8.3, implements a staggered decision workflow that sequentially maps hazard type and disaster stage to appropriate survey methods, sensor payloads, and compatible RPAS using rule-based and threshold-based filtering. RPAS selection considers payload capacity and range thresholds, adjusted dynamically by proximity, and ranks candidate platforms using hazard- and sensor-specific endurance criteria. The system is implemented using ArcGIS Pro 3.4.0, ArcGIS Experience Builder (2025 cloud release), and Azure Web App Services (Python 3.10 runtime). NDIS supports both batch processing and interactive real-time queries through a web-based user interface. Additional features include a statistical overview dashboard to help users interpret dataset distribution, and a crowdsourced input module that enables community-contributed hazard data via ArcGIS Survey123. NDIS is presented and validated in, for example, applications related to volcanic hazards in Indonesia. These capabilities make NDIS a scalable, adaptable, and operationally meaningful tool for multi-hazard monitoring and remote sensing mission planning. Full article

The importance of Last-Mile Delivery (LMD) in the current economy cannot be overstated, as it is the final and most crucial step in the supply chain between retailers and consumers. In major cities, absent intervention, urban LMD emissions are projected to rise by >30% by 2030 as e-commerce grows (top-100-city “do-nothing” baseline). Sustainable, innovative ground-based solutions for LMD, such as Electric Vehicles, autonomous delivery robots, parcel lockers, pick-up points, crowdsourcing, and freight-on-transit, can revolutionize urban logistics by reducing congestion and pollution while improving efficiency. However, developing these solutions presents challenges in Operations Research (OR), including problem modeling, optimization, and computations. This systematic review aims to provide an OR-centric synthesis of sustainable, ground-based LMD by (i) classifying these innovative solutions across problem types and methods, (ii) linking technique classes to sustainability goals (cost, emissions/energy, service, resilience, and equity), and (iii) identifying research gaps and promising hybrid designs. We support this synthesis by systematically screening 283 records (2010–2025) and analyzing 265 eligible studies. After the gap analysis, the researchers and practitioners are recommended to explore new combinations of innovative solutions for ground-based LMD. While they offer benefits, their complexity requires advanced solution algorithms and decision-making frameworks. Full article

Street design quality and socio-economic factors jointly influence housing prices, but their intertwined effects and spatial variations remain under-quantified. Housing prices not only reflect residents’ neighborhood experiences but also stem from the spillover value of public streets perceived and used by different users. This study takes Tianjin as a case and views the street environment as an immediate experience proxy for through-travelers, combining street view images and crowdsourced perception data to extract both subjective and objective indicators of the street environment, and integrating neighborhood and location characteristics. We use Geographical-XGBoost to evaluate the relative contributions of multiple factors to housing prices and their spatial variations. The results show that incorporating both subjective and objective street information into the Hedonic Pricing Model (HPM) improves its explanatory power, while local modeling with G-XGBoost further reveals significant heterogeneity in the strength and direction of effects across different locations. The results indicate that incorporating both subjective and objective street information into the HPM enhances explanatory power, while local modeling with G-XGBoost reveals significant heterogeneity in the strength and direction of effects across different locations. Street greening, educational resources, and transportation accessibility are consistently associated with higher housing prices, but their strength varies by location. Core urban areas exhibit a “counterproductive effect” in terms of complexity and recognizability, while peripheral areas show a “barely acceptable effect,” which may increase cognitive load and uncertainty for through-travelers. In summary, street environments and socio-economic conditions jointly influence housing prices via a “corridor-side–community-side” dual-pathway: the former (enclosure, safety, recognizability) corresponds to immediate improvements for through-travelers, while the latter (education and public services) corresponds to long-term improvements for residents. Therefore, core urban areas should control design complexity and optimize human-scale safety cues, while peripheral areas should focus on enhancing public services and transportation, and meeting basic quality thresholds with green spaces and open areas. Urban renewal within a 15 min walking radius of residential areas is expected to collaboratively improve daily travel experiences and neighborhood quality for both residents and through-travelers, supporting differentiated housing policy development and enhancing overall quality of life. Full article

Geospatial Artificial Intelligence (GeoAI) offers a scalable solution for automating the generation and updating of volunteered geographic information (VGI) maps—addressing the limitations of manual contributions to crowd-source mapping platforms such as OpenStreetMap (OSM). This study evaluates the accuracy of GeoAI-generated buildings specifically, using two Generative Adversarial Network (GAN) models. These are OSM-GAN—trained on OSM vector data and Google Earth imagery—and OSi-GAN—trained on authoritative “ground truth” Ordnance Survey Ireland (OSi) vector data and aerial orthophotos. Altogether, we assess map feature completeness, shape accuracy, and positional accuracy and conduct qualitative visual evaluations using live OSM database features and OSi map data as a benchmark. The results show that OSi-GAN achieves higher completeness (88.2%), while OSM-GAN provides more consistent shape fidelity (mean HD: 3.29 m; σ = 2.46 m) and positional accuracy (mean centroid distance: 1.02 m) compared to both OSi-GAN and the current OSM map. The OSM dataset exhibits moderate average deviation (mean HD 5.33 m) but high variability, revealing inconsistencies in crowd-source mapping. These empirical results demonstrate the potential of GeoAI to augment manual VGI mapping workflows to support timely downstream applications in urban planning, disaster response, and many other location-based services (LBSs). The findings also emphasize the need for robust Quality Assurance (QA) frameworks to address “AI slop” and ensure the reliability and consistency of GeoAI-generated data. Full article

The rapid evolution of Information Technologies is deeply transforming manufacturing, demanding innovative and enhanced production paradigms. Industry 5.0 further advances that transformation by fostering a more resilient, sustainable, and human-centric industrial ecosystem, built on the seamless integration of all value chains. This shift requires the timely collection and intelligent analysis of vast, heterogeneous data sources, including IoT devices, digital services, crowdsourcing platforms, and last but not least important human input, which is essential to drive innovation. With sustainability as a key priority, pervasive monitoring not only enables optimization to reduce greenhouse gas emissions but also plays a strategic role across the manufacturing economy. This work introduces Observability platform for Industry 5.0 (ObsI5), a novel observability framework specifically designed to support Industry 5.0 environments. ObsI5 extends cloud-native observability tools, originally developed for IT service monitoring, into manufacturing infrastructures, enabling the collection, analysis, and control of data across both IT and OT domains. Our solution integrates human contributions as active data sources and leverages standard observability practices to extract actionable insights from all available resources. We validate ObsI5 through a dedicated test bed, demonstrating its ability to meet the strict requirements of Industry 5.0 in terms of timeliness, security, and modularity. Full article

Background: Assistive technology (AT) can support autistic adults in navigating employment-related challenges. However, limited research has explored autistic adults’ actual needs and experiences with AT in the workplace. Existing studies often overlook how well current AT solutions align with the real-world demands autistic adults face across the employment process. To address this gap, this study conducted a needs assessment to explore autistic adults’ perceived AT and AT service needs across employment stages, identify satisfaction and discontinuation patterns, and examine barriers and facilitators to effective use. Methods: A total of 501 autistic adults were recruited through an online crowdsourcing platform, Prolific. Participants completed a needs assessment that included Likert-scale items and open-ended questions. Quantitative data were analyzed using descriptive statistics and weighted needs scoring procedures. Thematic analysis was applied to qualitative responses regarding satisfaction, discontinuation, and general reflections on AT use. Results: Job retention received the highest total weighted needs score, followed closely by skill development and job performance. Participants reported lower perceived needs for AT in the job development and placement domain. Qualitative findings revealed that AT was described as essential for daily functioning and independence, but barriers such as limited access, inadequate training, and social stigma affected use. Participants also emphasized the need for more person-centered and context-specific AT services. Conclusions: AT has the potential to significantly enhance employment outcomes for autistic adults. However, current services often lack personalization and alignment with real-world needs. Findings support the development of more inclusive, tailored, and accessible AT solutions across all employment stages. Full article

Aging infrastructure is a growing concern worldwide, with many bridges exceeding 50 years of service, prompting questions about their structural integrity. Over the past decade, the deterioration of bridges has driven extensive research into Structural Health Monitoring (SHM), a tool for early detection of structural deterioration, with particular emphasis on remote-sensing technologies. This review combines a scientometric analysis and a state-of-the-art review to assess recent advancements in the field. From a dataset of 702 publications (2014–2024), 171 relevant papers were analyzed, covering key SHM aspects including sensing devices, data acquisition, processing, damage detection, and reporting. Results show a 433% increase in publications, with the United States leading in output (28.65%), and Glisic, B., with collaborators forming the largest research cluster (11.7%). Accelerometers are the most commonly used sensors (50.88%), and data processing dominates the research focus (50.29%). Key challenges identified include cost (noted in 17.5% of studies), data corruption, and WSN limitations, particularly energy supply. Trends show a notable growth in AI applications (400%), and increasing interest in low-cost, crowdsource-based SHM using smartphones, MEMS, and cameras. These findings highlight both progress and future opportunities in SHM of footbridges. Full article

Identifying correspondences of road segments in different road networks, namely road-network matching, is an essential task for road network-centric data processing such as data integration of road networks and data quality assessment of crowd-sourced road networks. Traditional road-network matching usually relies on feature engineering and parameter selection of the geometry and topology of road networks for similarity measurement, resulting in poor performance when dealing with dense and irregular road network structures. Recent development of graph neural networks (GNNs) has demonstrated unsupervised modeling power on road network data, which learn the embedded vector representation of road networks through spatial feature induction and topology-based neighbor aggregation. However, weighting spatial information on the node feature alone fails to give full play to the expressive power of GNNs. To this end, this paper proposes a Spatial Pattern-aware Graph EMbedding learning method for road-network matching, named SP-GEM, which explores the idea of spatially-explicit modeling by identifying spatial patterns in neighbor aggregation. Firstly, a road graph is constructed from the road network data, and geometric, topological features are extracted as node features of the road graph. Then, four spatial patterns, including grid, high branching degree, irregular grid, and circuitous, are modelled in a sector-based road neighborhood for road embedding. Finally, the similarity of road embedding is used to find data correspondences between road networks. We conduct an algorithmic accuracy test to verify the effectiveness of SP-GEM on OSM and Tele Atlas data. The algorithmic accuracy experiments show that SP-GEM improves the matching accuracy and recall by at least 6.7% and 10.2% among the baselines, with high matching success rate (>70%), and improves the matching accuracy and recall by at least 17.7% and 17.0%, compared to the baseline GNNs, without spatially-explicit modeling. Further embedding analysis also verifies the effectiveness of the induction of spatial patterns. This study not only provides an effective and practical algorithm for road-network matching, but also serves as a test bed in exploring the role of spatially-explicit modeling in GNN-based road network modeling. The experimental performances of SP-GEM illuminate the path to develop GeoEmbedding services for geospatial applications. Full article