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 movement trajectories based on the past movement trajectories of 100,000 users[1]. Our team, "uclab2023", considered that model design significantly impacts training and prediction times in the task of human mobility trajectory prediction. To address this, we proposed a model based on BERT, commonly used in natural language processing, which allows parallel predictions, thus reducing both training and prediction times. In this challenge, Task 1 involves predicting the 15-day daily mobility trajectories of target users using the movement trajectories of 100,000 users. Task 2 focuses on predicting the 15-day emergency mobility trajectories of target users with data from 25,000 users. Our team achieved accuracy scores of GEOBLEU: 0.3440 and DTW: 29.9633 for Task 1 and GEOBLEU: 0.2239 and DTW: 44.7742 for Task 2[2][3].

This paper presents MinaQn1, a web-based participatory sensing platform to enhance bidirectional communication between municipality and citizens for citizen-centric urban development. To make cities more comfortable to live, it is necessary for municipality to collect and reflect citizens' demands in their administrative works. MinaQn enables city officers to make various questionnaire to citizen easily, and citizen can answer the questionnaire with their location information through any devices by using WEB interface. The answers from citizen are collected as sensor data with unified format and protocol, so that the data can be shared and used for various applications. We designed and implemented MinaQn with web and XMPP technology, and carried out a two weeks experiment in corporation with 3 cities in Japan. Based on the experiment, we measured the effectiveness of MinaQn with qualitative and quantitative data. In addition, we also evaluated how citizens consider about providing their location information to the administrative works of the cities.

Although object detection technology using cameras offers potential for various applications, it incurs dataset creation costs to train new models where general-purpose models are ineffective, such as in industrial settings. We have previously developed a semi-automated annotation framework that employs optical flow and representation learning techniques to reduce human effort significantly. However, it was likely to cause unintended annotation omissions and mistakes compared to manual annotation. In this study, we propose a composite image generation approach to create omission-free and pattern-rich datasets. The proposed method synthesizes natural-looking images without unannotated targets by placing labeled foreground segments at their original positions on targetless background frames collected with the same fixed-point cameras. Evaluation with video footage in a logistics warehouse confirmed that improved dataset reliability led to higher model performance.

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 movement prediction methods train deep learning models using large-scale movement histories from cities and make predictions. While cities with large users have sufficient training data, smaller cities with fewer users may face challenges due to insufficient data, raising concerns about lower prediction accuracy. Additionally, it is difficult to treat stay locations between different cities' movement histories due to differences in spatial area arrangement, making it challenging to share data across cities. To address this issue, we propose human movement prediction method that utilizes time-series stay frequency patterns, which can be commonly applied across different cities. This method demonstrated superiority in predicting movements in cities with fewer users compared to models trained and predicted using only the movement histories of the target city. Furthermore, the method achieved top 10 prediction accuracy in HuMob Challenge 2024.