Scalable Urban Crowdsensing: Data Contributor and Consumer Dynamics
Notice bibliographique
Résumé
Dynamic transportation routing and parking management rely on real-time data on traffic conditions and curbside availability. Traditional data collection methods require significant infrastructure investments and processing capacity, making them costly and often impractical for large-scale implementation. In contrast, crowdsensing leverages data from users’ smart devices, offering a cost-effective alternative for collecting real-time information. However, crowdsensed data is often noisy, inaccurate, unstructured, and heavily dependent on voluntary user contributions. This thesis is motivated by the central question: how can we effectively design and manage platforms, such as parking management and routing, that rely on such incomplete yet abundant data? To address this, several key studies are presented. The first study, Leveraging Data Contributors to Enhance Social Welfare Through Crowdsensing, examines how user-generated data can improve decision-making at service facilities such as parking lots. By aggregating reported wait times, the platform provides real-time estimates that help incoming users decide whether to enter. This research determines the optimal fraction of data contributors needed to maximize the collective benefit, social welfare, of facility users. The second study, Route Choice Using Crowd-Generated Travel Time Information, explores how crowdsensed travel times influence traffic assignment. Two critical factors, contribution ratio (the proportion of travelers sharing data) and observation window (the period over which data is collected), must be balanced to minimize network-wide travel times. This study analyzes their effects on perceived travel risks and proposes solutions to maintain their optimal values, ensuring efficiency in routing decisions. The third study, Resource Allocation and Route Generation for Urban Mobile Sensing, focuses on optimizing the number and routes of sensing agents, users carrying data collection devices, to enhance efficiency. By determining the optimal number of agents and their navigation paths, this study minimizes total travel costs while ensuring that each parking spot is revisited within the specified headway constraints. A case study in Toronto demonstrates the practical applicability of the proposed optimization framework for parking occupancy detection. The fourth study, Implementing Parking Occupancy Detection Using Dashcam Footage, develops a sensing system that analyzes dashcam video to detect real-time parking occupancy. By applying advanced video processing techniques, this project provides dynamic and scalable insights into urban parking availability. It aims to overcome the limitations of traditional parking data collection tools, such as stationary cameras and sensors, which face challenges related to installation, maintenance, and regulation. Instead, mobile technologies like dashcams, LiDAR, and ultrasound sensors offer a more scalable solution for capturing on-street parking availability. Together, these studies contribute to improving the efficiency of crowdsensing platforms by analyzing the impact of three types of crowdsensed data: wait times, travel times, and urban monitoring reports. These data types are examined across three distinct domains: service facilities (e.g., parking facilities), transportation networks (e.g., scenarios involving multiple modes of transportation or alternative routes with varying congestion levels), and urban monitoring (e.g., parking availability detection, pedestrian safety, and traffic flow monitoring). The research examines how access to such data influences individual decision-making, particularly in the presence of competing alternatives, as well as its impact on overall system performance. Furthermore, it investigates the factors affecting the accuracy and reliability of crowdsensed data in each domain and explores how optimizing these factors can enhance both user experience and system-wide efficiency.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,004 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
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