83 citations · 255 across the 24 of their papers we have counts for
24 papers
nD-RoPE: A Generalized RoPE for n-Dimensional Position Embedding
Boyang Li, Yulin Wu, Sizhe Xu +5
Rotary Position Embedding (RoPE) is widely adopted in Transformer models, yet its extension to high-dimensional domains lacks a unified theoretical formulation. Most existing appro…
Using large scale GPS data to reveal EV driver activity patterns beyond charging sessions
Callie Clark, Anne Driscoll, Xiyuan Ren +4
Accurate insights into electric vehicle (EV) driver behavior are essential for long-term infrastructure planning, grid management, and understanding downstream economic impacts, ye…
Disaster-induced behavioral change restructures social networks toward bonding ties
Vaidehi Raipat, Daniel Aldrich, Takahiro Yabe
Population displacement following environmental shocks reshapes the spatial organization of social interactions, often fragmenting existing ties and weakening community cohesion. A…
Assessing the Feasibility of a Video-Based Conversational Chatbot Survey for Measuring Perceived Cycling Safety: A Pilot Study in New York City
Feiyang Ren, Zhaoxi Zhang, Tamir Mendel +1
Bicycle safety is important for bikeability and transportation efficiency. However, conventional surveys often fall short in capturing how people actually perceive cycling environm…
MoE-TransMov: A Transformer-based Model for Next POI Prediction in Familiar & Unfamiliar Movements
Ruichen Tan, Jiawei Xue, Kota Tsubouchi +2
Accurate prediction of the next point of interest (POI) within human mobility trajectories is essential for location-based services, as it enables more timely and personalized reco…
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…
Behavioral changes during the pandemic worsened income diversity of urban encounters
Takahiro Yabe, Bernardo Garcia Bulle Bueno, Xiaowen Dong +2
Diversity of physical encounters and social interactions in urban environments are known to spur economic productivity and innovation in cities, while also to foster social capital…
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…
GEO-BLEU: Similarity Measure for Geospatial Sequences
Toru Shimizu, Kota Tsubouchi, Takahiro Yabe
In recent geospatial research, the importance of modeling large-scale human mobility data and predicting trajectories is rising, in parallel with progress in text generation using…
Multiwave COVID-19 Prediction from Social Awareness using Web Search and Mobility Data
J. Xue, T. Yabe, K. Tsubouchi +2
Recurring outbreaks of COVID-19 have posed enduring effects on global society, which calls for a predictor of pandemic waves using various data with early availability. Existing pr…
Mobile Phone Location Data for Disasters: A Review from Natural Hazards and Epidemics
Takahiro Yabe, Nicholas K W Jones, P Suresh C Rao +2
Rapid urbanization and climate change trends are intertwined with complex interactions of various social, economic, and political factors. The increased trends of disaster risks ha…
Location Data Reveals Disproportionate Disaster Impact Amongst the Poor: A Case Study of the 2017 Puebla Earthquake Using Mobilkit
Takahiro Yabe, Nicholas K W Jones, Nancy Lozano-Gracia +4
Location data obtained from smartphones is increasingly finding use cases in disaster risk management. Where traditionally, CDR has provided the predominant digital footprint for h…
Mobilkit: A Python Toolkit for Urban Resilience and Disaster Risk Management Analytics using High Frequency Human Mobility Data
Enrico Ubaldi, Takahiro Yabe, Nicholas K. W. Jones +4
Increasingly available high-frequency location datasets derived from smartphones provide unprecedented insight into trajectories of human mobility. These datasets can play a signif…
Resilience of Interdependent Urban Socio-Physical Systems using Large-Scale Mobility Data: Modeling Recovery Dynamics
Takahiro Yabe, P. Suresh C. Rao, Satish V. Ukkusuri
Cities are complex systems comprised of socioeconomic systems relying on critical services delivered by multiple physical infrastructure networks. Due to interdependencies between…
Mobility-based contact exposure explains the disparity of spread of COVID-19 in urban neighborhoods
Rajat Verma, Takahiro Yabe, Satish V. Ukkusuri
The rapid early spread of COVID-19 in the U.S. was experienced very differently by different socioeconomic groups and business industries. In this study, we study aggregate mobilit…
Quantifying Spatial Homogeneity of Urban Road Networks via Graph Neural Networks
Jiawei Xue, Nan Jiang, Senwei Liang +4
Quantifying the topological similarities of different parts of urban road networks (URNs) enables us to understand the urban growth patterns. While conventional statistics provide…
Early Warning of COVID-19 Hotspots using Mobility of High Risk Users from Web Search Queries
Takahiro Yabe, Kota Tsubouchi, Satish V Ukkusuri
COVID-19 has disrupted the global economy and well-being of people at an unprecedented scale and magnitude. To contain the disease, an effective early warning system that predicts…
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…
Quantifying the Economic Impact of Extreme Shocks on Businesses using Human Mobility Data: a Bayesian Causal Inference Approach
Takahiro Yabe, Yunchang Zhang, Satish Ukkusuri
In recent years, extreme shocks, such as natural disasters, are increasing in both frequency and intensity, causing significant economic loss to many cities around the world. Quant…
Learning Fine Grained Place Embeddings with Spatial Hierarchy from Human Mobility Trajectories
Toru Shimizu, Takahiro Yabe, Kota Tsubouchi
Place embeddings generated from human mobility trajectories have become a popular method to understand the functionality of places. Place embeddings with high spatial resolution ar…
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…
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…