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The 2nd ACM SIGSPATIAL International Workshop on Advances in Urban AI (Urban-AI 2024) brings together researchers and practitioners to discuss advancements and future directions in urban AI. Urban AI is an emerging field that combines AI, spatial computing, and urban science to address complex challenges faced by cities. The availability of extensive urban data and the growth of digitized city infrastructures have opened opportunities for data-driven machine learning approaches in urban sciences. Urban AI encompasses innovative AI techniques applied to urban problems, AI-ready urban data infrastructure, and various urban applications benefiting from AI. Its applications range from urban planning and design to traffic prediction, energy management, public safety, urban agriculture, and land use.
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A Graph Deep Learning Model for Station Ridership Prediction in Expanding Metro Networks
Due to their reliability, efficiency, and environmental friendliness, metro systems have become a crucial solution to transportation challenges associated with urbanization. Many countries have constructed or expanded their metro networks over the past ...
Smart Route: A GIS-Based Solution for Mass Transit Design and Optimization
Mass transit is a key aspect of urban planning and management. A vast network of mass transit provides various options for connectivity to individuals through extensive networks. On the other hand, a bigger network incurs a huge cost on the operator. ...
An Advance Review of Urban-AI and Ethical Considerations
The rapid digitization of urban infrastructure and the availability of urban data have created opportunities for developing and using artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms to address cities' difficult ...
Generative-AI based Map Representation and Localization
In the domain of Image-Based Localization (IBL), the precise integration of street-level and satellite perspectives plays a pivotal role, particularly in dynamic urban environments. This research introduces a novel generative AI framework that ...
Automating Bibliometric Analysis with Sentence Transformers and Retrieval-Augmented Generation (RAG): A Pilot Study in Semantic and Contextual Search for Customized Literature Characterization for High-Impact Urban Research
Bibliometric analysis is essential for understanding research trends, scope, and impact in urban science, especially in high-impact journals, such Nature Portfolios. However, traditional methods, relying on keyword searches and basic NLP techniques, ...
Encryption Techniques for Privacy-Preserving CNN Models: Performance and Practicality in Urban AI Applications
In recent years, as urban AI applications increasingly rely on sensitive data, ensuring the privacy and security of machine learning (ML) models has become essential. The proposed research study evaluates the performance and security trade-offs of seven ...
SurfaceAI: Automated creation of cohesive road surface quality datasets based on open street-level imagery
This paper introduces SurfaceAI, a pipeline designed to generate comprehensive georeferenced datasets on road surface type and quality from openly available street-level imagery. The motivation stems from the significant impact of road unevenness on the ...
MapYog - Intelligent Spatiotemporal Data Explorer
MapYog addresses the critical challenge of managing and analyzing heterogeneous, multi-granular geospatial data, a key issue in urban planning, environmental monitoring, and various geospatial applications. Existing systems often struggle to integrate ...
Optimization of Site Selection for Free-Floating Shared Electric Vehicles Based on Deep Reinforcement Learning
- Shaohua Wang,
- Xiaojian Liang,
- Liang Zhou,
- Xiao Li,
- Yongyi Pan,
- Yin Cheng,
- Chunxiang Cao,
- Cheng Su,
- Jiayi Zheng
As a contemporary mode of transportation for medium-to-long distances, the widespread adoption of free-floating shared electric vehicles has the potential to reduce urban carbon emissions and alleviate traffic congestion. However, this mode of ...
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- Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Urban-AI
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Acceptance Rates
Year | Submitted | Accepted | Rate |
---|---|---|---|
UrbanAI '24 | 12 | 9 | 75% |
Overall | 12 | 9 | 75% |