Search Results (190)

Urban mobility has undergone a significant transformation in recent years, caused by rapid urbanization, environmental pressures, and technological innovation. Even though digital tools and mobility platforms are increasingly used to address transportation challenges, these challenges remain complex and multidimensional, concerning not only infrastructure, but also user behavior, institutional coordination, trust, and social acceptance. Crowdsourcing has proven effective in leveraging distributed knowledge and accelerating innovation in business and public sectors. However, its application in urban mobility contexts has not yet been sufficiently synthesized in a framework-oriented manner. To address this, the study first conducted a comprehensive literature review of existing crowdsourcing assessment frameworks and their applicability to mobility systems. The results show that current implementations in urban mobility often remain fragmented and limited to unidirectional data extraction, lacking comprehensive approaches that integrate technological, social, and organizational dimensions. In response to this, the authors developed the SMART-CROWD framework for assessing cities’ maturity in using crowdsourcing across six dimensions: Strategy & Leadership (S), Methods & Tools (M), Engagement & Representativeness (A), Responsiveness & Impact (R), Technology & Data (T), and Civic Capital & Sustainability (CROWD). Each dimension includes measurable indicators, providing a structured basis of diagnosing disparities between technological capabilities and socio-institutional readiness. The SMART-CROWD framework is intended to support a transition from one-way data acquisition toward more scalable, reciprocal, and citizen-focused innovation ecosystems. This work contributes to the field of applied systems innovation by proposing a structured framework for assessing and guiding the use of distributed intelligence in smart urban mobility. Full article

Crowdsourcing contest platforms provide enterprises with opportunities to seek external resources at a lower cost. Increasing the participation of solvers is the key to improving the success of crowdsourcing contests. Many previous studies attempt to use participation intention as a proxy for exploring participation behavior, using surveys to examine participation intention or continued participation intention. However, participation intention and participation behavior differ significantly, and the dynamic nature of influencing factors has a more complex effect on solvers’ participation. Therefore, based on social exchange theory, we use the dynamic data of winvk.com to construct a two-stage model of view and submission. The effect of dynamic competition on participation intention and participation behavior is explored. The results show that external competition has a consistent negative effect on both participation intention and participation behavior. However, the effect of internal competition is different. It has no significant effect on participation intention, but has a significant positive effect on participation behavior. In addition, rewards exacerbate the effect of competition on participation behavior. These findings provide empirical evidence for exploring differences in participation intention and behavior, and offer practical suggestions for enterprises and platforms to improve solvers’ participation in a short time. Full article

Platforms often fail to incorporate the needs of white hats who prefer non-material incentives when designing reward schemes. To study incentive design under private preference information, this paper develops a dynamic game with incomplete information and examines how appointing communications specialists can facilitate truthful preference disclosure and improve the platform’s incentive strategy. The results indicate that the platform’s decision depends on the white hats’ net utility from participation, white hats’ reputational losses, and the platform’s prior probability that white hats participate. When the platform appoints communications specialists, white hats disclose their preference information truthfully once their net utility from participation exceeds a threshold. Under this condition, the platform can identify their preference types and match incentive types to white hats’ preferences. Under misaligned signaling, where white hats use material (non-material) incentives to signal non-material (material) preferences, the platform has no incentive to appoint communications specialists. Full article

The rapid growth of social networks and online platforms has heightened the importance of trust evaluation in various applications, including e-commerce, social networking, online collaboration, and mobile crowdsourcing. Traditional trust evaluation methods often rely on handcrafted features and simple models, which fail to fully capture the implicit patterns within the complex, heterogeneous structures of social networks. To address this issue, we propose TrustGTN, a novel method based on Heterogeneous Graph Neural Networks (HGNNs). It incorporates a soft selection mechanism that dynamically adjusts the training matrix weights. This enables it to capture the evolving structural and semantic patterns of the graph. The model can automatically learn important trust chains without the need to manually set their lengths. Experimental results show that TrustGTN outperforms existing trust evaluation methods on public datasets, demonstrating its advantages in handling heterogeneous graph data. Full article

Crowdsourced geospatial platforms constitute complex socio-technical systems in which data quality and reliability emerge from collective user behavior rather than centralized control. This study proposes a system-oriented, unsupervised machine learning framework to assess the reliability of crowdsourced building data using only intrinsic indicators. The framework is demonstrated through a large-scale analysis of OpenStreetMap building polygons in Tehran. Six intrinsic indicators—reflecting contributor activity, temporal dynamics, semantic instability, and geometric evolution—were normalized using fuzzy membership functions and objectively weighted based on their discriminative influence within a K-means clustering process. Five reliability classes were identified, ranging from very low to very high reliability. The resulting classification exhibited strong internal validity (average silhouette coefficient = 0.58) and pronounced spatial coherence (Global Moran’s I = 0.85, p < 0.001). This approach eliminates dependence on authoritative reference datasets, enabling scalable, reproducible, and feature-level reliability assessment in open geospatial systems. The framework provides a transferable methodological foundation for trust-aware analysis and decision-making in participatory and data-intensive systems. Full article

With the widespread deployment of Industrial Cyber-Physical Systems (ICPS), their inherent vulnerabilities have increasingly exposed them to sophisticated cybersecurity threats. Although existing protective mechanisms can block attacks at runtime, the risk of defense failure remains. To proactively evaluate and harden ICPS security, we design a distributed crowdsourced testing platform tailored to the four-layer cloud ICPS architecture—spanning the workshop, factory, enterprise, and external network layers. Building on this architecture, we develop a Distributed Input–Output Testing and Verification Framework (DIOTVF) that models ICPS as systems with spatially separated injection and observation points, and supports controllable communication delays and multithreaded parallel execution. The framework incorporates a dynamic test–task management model, an asynchronous concurrent testing mechanism, and an optional LLM-assisted thread controller, enabling efficient scheduling of large testing workloads under asynchronous network conditions. We implement the proposed framework in a prototype platform and deploy it on a virtualized ICPS testbed with configurable delay characteristics. Through a series of experimental validations, we demonstrate that the proposed framework can improve testing and verification speed by approximately 2.6 times compared to Apache JMeter. Full article

This study presents the e[kent-im] model, a map-based crowdsourcing initiative that digitizes and safeguards urban memory and cultural heritage through community participation and digital tools. The model facilitates the collection, archiving, and dissemination of urban memories by fostering intergenerational knowledge transfer and encouraging civic engagement in heritage preservation. Implemented in the historical center of Kütahya/Türkiye, the project gathered 150 memories and stories from 12 senior participants aged 50–85, which were linked to 303 historical visuals sourced from personal archives. These materials were integrated into a custom-designed web and mobile interface (Mapotic Pro) enriched with metadata categories such as type, period, and location, enabling users to filter and navigate content effectively and watch the videos enriched with participant narratives. A digital city archive matrix was also developed to systematically organize the collected data and support the web-based platform. To assess the platform’s effectiveness, a pilot study with 15 young participants aged 18–28 was conducted. During a self-guided city tour, participants engaged with historical content on the platform and provided feedback through pre- and post-test evaluations. Results indicated heightened awareness of and interest in cultural heritage, demonstrating the model’s potential as both an interactive archive and a tool facilitating intergenerational heritage awareness. Overall, this study highlights the model’s adaptability, scalability, and capacity to bridge generational and technological divides. 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

Driven by the green and low-carbon transformation of urban logistics, the integration of crowdsourced delivery and green transportation is considered an important pathway to achieving sustainable last-mile delivery. This study focuses on urban crowdsourced delivery using cargo bikes and develops an extended behavioral model based on the Theory of Planned Behavior (TPB). The model systematically examines the key factors influencing the public’s behavioral intention (BI) to participate as crowdshippers. While retaining the core structure of TPB, the model incorporates external variables—perceived risk (PR), policy support (PS), and infrastructure conditions (IC)—to improve its explanatory power and applicability to real-world delivery scenarios. A questionnaire survey was conducted in South Korea, yielding 600 valid responses. The results indicate that usage attitude and perceived behavioral control exert significant positive effects on BI. PR has a significant negative effect on both attitude and BI. PS indirectly enhances BI by improving attitudes, whereas IC primarily influences BI by strengthening the public’s sense of control. This study not only expands the theoretical explanatory power of the TPB model in the context of green crowdsourced delivery but also provides empirical evidence for policymakers and platform operators. Full article

This paper explores how digital crowdsourcing platforms communicate sustainability-oriented innovation and mobilise stakeholder engagement. Through a directed content analysis of three platforms (OpenIDEO, San Francisco, CA, USA; Enel Innovation Hub, Rome, Italy; and InnoCentive, Waltham, MA, USA). The study examines communication strategies, participation models, and alignment with the United Nations Sustainable Development Goals (SDGs). Results show that communication is not neutral but functions as a governance mechanism shaping who participates, how innovation is framed, and what outcomes emerge. OpenIDEO fosters inclusive co-creation and SDG alignment, Enel Innovation Hub highlights technical readiness and energy transition, and InnoCentive relies on rewards and competition. Word-frequency analysis confirms these emphases, while interpretation through Motivation Crowding Theory, Social Exchange Theory, and Transaction Cost Theory explains how motivational framing, legitimacy signals, and participation structures affect engagement. The study contributes to research on open innovation and platform studies by demonstrating the constitutive role of communication in enabling or constraining sustainable collective action. Practical implications are outlined for platform designers, marketers, and policymakers seeking to align digital infrastructures with systemic sustainability goals. 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

The explosive growth of tasks on crowdsourcing platforms has intensified information overload, making it difficult for workers to spot lucrative bids; yet mainstream recommenders inherit a user-independence assumption from e-commerce and therefore overlook the real-time competition among workers, which degrades ranking stability and accuracy. To bridge this gap, we propose the Adaptive Parameter Exponential Decay Algorithm (APAED), which first produces base relevance scores with an offline neural model and then injects a competition-aware exponential decay whose strength is jointly determined by the interquartile range of each worker’s score list (global factor) and the live bid distribution of every task (local factor). This model-agnostic adjustment explicitly quantifies competitive intensity without handcrafted features and can be paired with any backbone recommender. Experiments on a real-world dataset comprising 25,643 tasks and 19,735 workers show that APAED cuts the residual RMSE of HR@10 from $9.575\times {10}^{-4}$ to $5.939\times {10}^{-4}$ (−38%) and that of MRR from $2.920\times {10}^{-4}$ to $0.736\times {10}^{-4}$ (−75%), substantially reducing score fluctuations across epochs and consistently outperforming four strong neural baselines. These results confirm that explicitly modeling worker competition yields more accurate and stable task recommendations in crowdsourcing environments. 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

Ongoing changes in the distribution and abundance of several tick species of medical relevance in Canada have prompted the development of the eTick platform—an image-based crowd-sourcing public surveillance tool for Canada enabling rapid tick species identification by trained personnel, and public health guidance based on tick species and province of residence of the submitter. Considering that more than 100,000 images from over 73,500 identified records representing 25 tick species have been submitted to eTick since the public launch in 2018, a partial automation of the image processing workflow could save substantial human resources, especially as submission numbers have been steadily increasing since 2021. In this study, we evaluate an end-to-end artificial intelligence (AI) pipeline to support tick identification from eTick user-submitted images, characterized by heterogeneous quality and uncontrolled acquisition conditions. Our framework integrates (i) tick localization using a fine-tuned YOLOv7 object detection model, (ii) resolution enhancement of cropped images via super-resolution Generative Adversarial Networks (RealESRGAN and SwinIR), and (iii) image classification using deep convolutional (ResNet-50) and transformer-based (ViT) architectures across three datasets (12, 6, and 3 classes) of decreasing granularities in terms of taxonomic resolution, tick life stage, and specimen viewing angle. ViT consistently outperformed ResNet-50, especially in complex classification settings. The configuration yielding the best performance—relying on object detection without incorporating super-resolution—achieved a macro-averaged F1-score exceeding 86% in the 3-class model (Dermacentor sp., other species, bad images), with minimal critical misclassifications (0.7% of “other species” misclassified as Dermacentor). Given that Dermacentor ticks represent more than 60% of tick volume submitted on the eTick platform, the integration of a low granularity model in the processing workflow could save significant time while maintaining very high standards of identification accuracy. Our findings highlight the potential of combining modern AI methods to facilitate efficient and accurate tick image processing in community science platforms, while emphasizing the need to adapt model complexity and class resolution to task-specific constraints. Full article