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Multiple Areal Feature Aware Transportation Demand Prediction

Autor: Han, Sumin, An, Jisun, Park, Youngjun, Kim, Suji, Jang, Kitae, Lee, Dongman

A reliable short-term transportation demand prediction supports the authorities in improving the capability of systems by optimizing schedules, adjusting fleet sizes, and generating new transit networks. A handful of research efforts incorporate one

Externí odkaz: http://arxiv.org/abs/2408.12890

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Spatio-Temporal Road Traffic Prediction using Real-time Regional Knowledge

Autor: Han, Sumin, An, Jisun, Lee, Dongman

For traffic prediction in transportation services such as car-sharing and ride-hailing, mid-term road traffic prediction (within a few hours) is considered essential. However, the existing road-level traffic prediction has mainly studied how signific

Externí odkaz: http://arxiv.org/abs/2408.12882

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Improving Real Estate Appraisal with POI Integration and Areal Embedding

Autor: Han, Sumin, Park, Youngjun, Sabir, Sonia, An, Jisun, Lee, Dongman

Despite advancements in real estate appraisal methods, this study primarily focuses on two pivotal challenges. Firstly, we explore the often-underestimated impact of Points of Interest (POI) on property values, emphasizing the necessity for a compreh

Externí odkaz: http://arxiv.org/abs/2311.11812

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Enhancing Spatiotemporal Traffic Prediction through Urban Human Activity Analysis

Autor: Han, Sumin, Park, Youngjun, Lee, Minji, An, Jisun, Lee, Dongman

Traffic prediction is one of the key elements to ensure the safety and convenience of citizens. Existing traffic prediction models primarily focus on deep learning architectures to capture spatial and temporal correlation. They often overlook the und

Externí odkaz: http://arxiv.org/abs/2308.10282

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