877 citations · 1k across the 14 of their papers we have counts for
16 papers
TimeES: Probabilistic and Deterministic Time Series Forecasting via Evolutionary Spectra
Weiwei Ye, Renhe Jiang, Hangchen Liu +2
Real-world time series are inherently non-stationary, with trends, periodic patterns, and uncertainty evolving over time. While the Fourier domain offers a natural lens to model ti…
DiffPTS: Rethinking Diffusion ELBO for Probabilistic Time Series Forecasting
Weiwei Ye, Dongyuan Li, Hangchen Liu +3
Probabilistic time series forecasting requires modeling and predicting complex and time-varying distributions. Recently, Denoising Diffusion Probabilistic Model (DDPM)-based approa…
Synthetic Human Mobility Data Generation: A Structured Review of Representations, Methods, and Practical Capabilities
Yanbo Pang, Chen Zhong, Song Gao +1
Human mobility data has become an increasingly important component of urban analytics. Although the range of available mobility data sources has expanded substantially, access rema…
TimeClaw: A Time-Series AI Agent with Exploratory Execution Learning
Hangchen Liu, Dongyuan Li, Renhe Jiang +3
Time series analysis underpins forecasting, monitoring, and decision making in domains such as finance and weather, where solving a task often requires both numerical accuracy and…
Explainable Hierarchical Urban Representation Learning for Commuting Flow Prediction
Mingfei Cai, Yanbo Pang, Yoshihide Sekimoto
Commuting flow prediction is an essential task for municipal operations in the real world. Previous studies have revealed that it is feasible to estimate the commuting origin-desti…
Metropolitan Scale and Longitudinal Dataset of Anonymized Human Mobility Trajectories
Takahiro Yabe, Kota Tsubouchi, Toru Shimizu +4
Modeling and predicting human mobility trajectories in urban areas is an essential task for various applications. The recent availability of large-scale human movement data collect…
Road Rutting Detection using Deep Learning on Images
Poonam Kumari Saha, Deeksha Arya, Ashutosh Kumar +2
Road rutting is a severe road distress that can cause premature failure of road incurring early and costly maintenance costs. Research on road damage detection using image processi…
Pseudo-PFLOW: Development of nationwide synthetic open dataset for people movement based on limited travel survey and open statistical data
Takehiro Kashiyama, Yanbo Pang, Yoshihide Sekimoto +1
People flow data are utilized in diverse fields such as urban and commercial planning and disaster management. However, people flow data collected from mobile phones, such as using…
Development of current estimated household data and agent-based simulation of the future population distribution of households in Japan
Kajiwara Kento, Jue Ma, Toshikazu Seto +3
In response to the declining population and aging infrastructure in Japan, local governments are implementing compact city policies such as the location normalization plan. To opti…
Non-Compulsory Measures Sufficiently Reduced Human Mobility in Tokyo during the COVID-19 Epidemic
Takahiro Yabe, Kota Tsubouchi, Naoya Fujiwara +3
While large scale mobility data has become a popular tool to monitor the mobility patterns during the COVID-19 pandemic, the impacts of non-compulsory measures in Tokyo, Japan on h…
City2City: Translating Place Representations across Cities
Takahiro Yabe, Kota Tsubouchi, Toru Shimizu +2
Large mobility datasets collected from various sources have allowed us to observe, analyze, predict and solve a wide range of important urban challenges. In particular, studies hav…
Congestion Analysis of Convolutional Neural Network-Based Pedestrian Counting Methods on Helicopter Footage
Gergely Csönde, Yoshihide Sekimoto, Takehiro Kashiyama
Over the past few years, researchers have presented many different applications for convolutional neural networks, including those for the detection and recognition of objects from…
Predicting Evacuation Decisions using Representations of Individuals' Pre-Disaster Web Search Behavior
Takahiro Yabe, Kota Tsubouchi, Toru Shimizu +2
Predicting the evacuation decisions of individuals before the disaster strikes is crucial for planning first response strategies. In addition to the studies on post-disaster analys…
Universality of population recovery patterns after disasters
Takahiro Yabe, Kota Tsubouchi, Naoya Fujiwara +2
Despite the rising importance of enhancing community resilience to disasters, our understanding on how communities recover from catastrophic events is limited. Here we study the po…
Cross-comparative analysis of evacuation behavior after earthquakes using mobile phone data
Takahiro Yabe, Yoshihide Sekimoto, Kota Tsubouchi +1
Despite the importance of predicting evacuation mobility dynamics after large scale disasters for effective first response and disaster relief, our general understanding of evacuat…
Road Damage Detection Using Deep Neural Networks with Images Captured Through a Smartphone
Hiroya Maeda, Yoshihide Sekimoto, Toshikazu Seto +2
Research on damage detection of road surfaces using image processing techniques has been actively conducted, achieving considerably high detection accuracies. Many studies only foc…