skip to main content
10.1145/3681771acmconferencesBook PagePublication PagesgisConference Proceedingsconference-collections
HuMob'24: Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Human Mobility Prediction Challenge
ACM2024 Proceeding
Publisher:
  • Association for Computing Machinery
  • New York
  • NY
  • United States
Conference:
SIGSPATIAL '24: The 32nd ACM International Conference on Advances in Geographic Information Systems Atlanta GA USA 29 October 2024- 1 November 2024
ISBN:
979-8-4007-1150-3
Published:
16 December 2024
Sponsors:
Recommend ACM DL
ALREADY A SUBSCRIBER?SIGN IN

Reflects downloads up to 08 Feb 2025Bibliometrics
Skip Abstract Section
Abstract

The Human Mobility Prediction Challenge (HuMob Challenge) 2024 (https://wp.nyu.edu/humobchallenge2024/) is a competition aiming at testing models for the prediction of human mobility patterns, using an open source, urban scale (10K~100K individuals per city), longitudinal (75 days) trajectory dataset, across 4 metropolitan areas.

Skip Table Of Content Section
short-paper
Instruction-Tuning Llama-3-8B Excels in City-Scale Mobility Prediction

Human mobility prediction plays a critical role in applications such as disaster response, urban planning, and epidemic forecasting. Traditional methods often rely on designing crafted, domain-specific models, and typically focus on short-term ...

short-paper
Time-series Stay Frequency for Multi-City Next Location Prediction using Multiple BERTs

Human Mobility Prediction Challenge 2024 was organized to compare human future movement prediction methods using a unified dataset. The challenge focuses on human movement prediction in multiple cities with varying numbers of users. Many existing ...

short-paper
Open Access
ST-MoE-BERT: A Spatial-Temporal Mixture-of-Experts Framework for Long-Term Cross-City Mobility Prediction

Predicting human mobility across multiple cities presents significant challenges due to the complex and diverse spatial-temporal dynamics inherent in different urban environments. In this study, we propose a robust approach to predict human mobility ...

short-paper
Human Mobility Prediction using Day of the Week probability

In this paper, we describe our method used at the prediction for the Human Mobility Prediction Challenge (HuMob Challenge) 2024 [1]. Here, we utilized the fact that human behavior patterns seem to depend on cyclical rhythms. We improved a simple weekly ...

short-paper
Open Access
The Story of Mobility: Combining State Space Models and Transformers for Multi-Step Trajectory Prediction

Machine learning models for predicting human mobility often require large datasets for training, which are not always available. As a result, methods capable of learning from limited data are essential. The Human Mobility Challenge 2024 was designed to ...

short-paper
Open Access
Using the Temporal-Trajectory-based K Nearest Neighbor Algorithm to Predict Human Mobility Patterns

Researchers are increasingly applying AI to predict the daily routines of individuals and their corresponding trajectories (referred to as mobility prediction), due to its considerable potential for commercial activities and public administration. ...

short-paper
Human Mobility Prediction using Personalized Spatiotemporal Models

In this paper, I propose personalized spatiotemporal models based human mobility prediction method. The proposed method consists of three steps. Step1: we sin-cos transform the date and time data, and at the same time create variables representing ...

short-paper
Open Access
Cross-city-aware Spatiotemporal BERT

Predicting human mobility has been actively studied for the past decade because of its various possible applications, such as traffic optimization and urban planning. Despite the increasing interest in human mobility prediction, the training and ...

short-paper
Urban Human Mobility Prediction Using Support Vector Regression: A Classical Data-Driven Approach

This paper presents an efficient method for predicting human mobility trajectories in urban areas of Japan, developed for the Hu-MobChallenge2024. Utilizing large-scale human mobility data, we constructed personalized models for individual users, ...

short-paper
Open Access
Personalized and On-device Trajectory Mobility Prediction

Accurately predicting individual trajectory mobility is critical for various urban applications, including traffic management and personalized services. However, existing deep learning models often suffer from overfitting due to noisy, large-scale ...

short-paper
Multiple Systems Combination to Improve Human Mobility Prediction

This paper describes a human mobility prediction system which build by KDDI Research, Inc. to submit for Human Mobility Prediction Challenge (HuMob Challenge) 2024. The system consists of 6 human mobility prediction subsystems: 5 transformer-based ...

short-paper
Trajectory Prediction Using Random Forests with Time Decay and Periodic Features

In the 2024 Human Movement Trajectory Prediction Competition, contestants need to predict individual movement trajectories for the next 15 days based on data from four metropolitan areas in Japan. This article proposes a prediction method based on single ...

short-paper
CrossBag: A Bag of Tricks for Cross-City Mobility Prediction

Access to large-scale human trajectory data has significantly advanced the understanding of human mobility (HuMob) behavior for urban planning. However, these data are often concentrated in major cities, leaving smaller or less-monitored areas with ...

short-paper
Open Access
Human Mobility Challenge: Are Transformers Effective for Human Mobility Prediction?

Transformer-based models are popular for time series forecasting and spatiotemporal prediction due to their ability to infer semantic correlations in long sequences. However, for human mobility prediction, temporal correlations, such as location patterns ...

Contributors
  • Massachusetts Institute of Technology
  • Yahoo Japan Corporation
  • Yahoo Japan Corporation
Index terms have been assigned to the content through auto-classification.

Recommendations