Understanding, modeling, and predicting human mobility trajectories in urban areas is an essential task for various domains and applications, including transportation modeling, disaster risk management, and urban planning. The recent availability of large-scale human movement and behavior data collected from (often millions of) mobile devices and social media platforms have enabled the development and testing of complex human mobility models, resulting in a plethora of methods published in computer science venues such as ACM SIGSPATIAL.

However, human mobility prediction methods are trained and tested on different datasets due to the lack of open-source and large-scale human mobility datasets amid privacy concerns, making it difficult to make fair comparisons of other methods’ performances. The lack of large-scale open-source datasets has been one of the key barriers hindering the progress of human mobility model development.

Following the success of HuMob Challenge 2023 @ ACM SIGSPATIAL 2023, we will host HuMob Challenge 2024 using a synthetic but realistic human mobility dataset of 6K~100K individuals’ trajectories per city, across 75 days in 4 metropolitan areas provided by LY Corporation (previously Yahoo Japan). Participants will develop and test methods to predict human mobility trajectories using the provided open-source dataset.