2023 Proceeding
The Human Mobility Prediction Challenge (HuMob Challenge) 2023 (https://connection.mit.edu/humob-challenge-2023) is a competition aiming at testing models for the prediction of human mobility patterns, using an open source, urban scale (100K individuals), longitudinal (90 days) trajectory dataset.
Proceeding Downloads
Human Mobility Prediction Challenge: Next Location Prediction using Spatiotemporal BERT
- Haru Terashima,
- Naoki Tamura,
- Kazuyuki Shoji,
- Shin Katayama,
- Kenta Urano,
- Takuro Yonezawa,
- Nobuo Kawaguchi
Understanding, modeling, and predicting human mobility patterns in urban areas has become a crucial task from the perspectives of traffic modeling, disaster risk management, urban planning, and more. HuMob Challenge 2023 aims to predict future ...
Modeling and generating human mobility trajectories using transformer with day encoding
Modeling and predicting human mobility trajectories in urban areas is a crucial challenge with various applications. The HuMob Challenge is a competition that focuses on modeling human mobility trajectories using large open-source datasets. This paper ...
GeoFormer: Predicting Human Mobility using Generative Pre-trained Transformer (GPT)
Predicting human mobility holds significant practical value, with applications ranging from enhancing disaster risk planning to simulating epidemic spread. In this paper, we present the GeoFormer, a decoder-only transformer model adapted from the GPT ...
Large-Scale Human Mobility Prediction Based on Periodic Attenuation and Local Feature Match
With the rapid advancement of technology, modeling travel behavior based on large-scale mobile trajectory data has become a core issue in urban traffic management. Mining complex patterns of travel behavior and modeling based on this to explain, ...
Personalized human mobility prediction for HuMob challenge
We explain the methodology used to create the data submitted to HuMob Challenge, a data analysis competition for human mobility prediction. We adopted a personalized model to predict the individual's movement trajectory from their data, instead of ...
Estimating future human trajectories from sparse time series data
In this paper, we address the Human Mobility Prediction Challenge (HuMob Challenge) 2023, focusing on the prediction of human mobility patterns using sparse datasets, a prevalent challenge impacting urban planning, disaster risk management, and ...
Multi-perspective Spatiotemporal Context-aware Neural Networks for Human Mobility Prediction
Accurately predicting human mobility is crucial for understanding human dynamics, addressing urban challenges, and improving urban planning. However, it still faces challenges due to the dynamic and complex nature of human mobility. Traditional ...
Cell-Level Trajectory Prediction Using Time-embedded Encoder-Decoder Network
Predicting human mobility is essential in various domains and applications (e.g., urban planning) and is one of the fundamental research topics in GIS. In recent years, there has been a lot of research on mobility (and trajectory) prediction using ...
Forecasting Urban Mobility using Sparse Data: A Gradient Boosted Fusion Tree Approach
Predicting human mobility in urban landscape poses complex challenges, especially when navigating sporadic mobility datasets. This paper introduces a robust predictive model founded on gradient boosted decision trees, designed to forecast human ...
Batch and negative sampling design for human mobility graph neural network training
This study presents a deep learning approach to human mobility prediction within a network science framework. We proposed a human mobility graph that defines two types of nodes---people and locations---and employs domain-specific attributes to capture ...