Human Mobility & Urban Flow Prediction
TL;DR
The current study aims to present a pragmatic solution to predicting crowd density through a data-driven approach that involves machine learning and contextual logic and shows the benefits of applying machine learning together with logical rules to urban mobility forecasting problems.
Abstract
The prediction of human movement and crowd density is critical for enhancing urban planning and managing traffic in smart cities. Due to the rise of digital technologies and location data, it has been easier to track how humans interact in various geographical locations over different time intervals. Unfortunately, static methods and straightforward statistical models cannot predict crowd movement since they are too rigid to capture the dynamism of real-world scenarios. The current study aims to present a pragmatic solution to predicting crowd density through a data-driven approach that involves machine learning and contextual logic. The algorithm relies on location, date, and time inputs to identify key features related to temporal factors, seasonality, and location attributes. Predictions are initially generated by employing a random forest regression method before optimizing results using behavioral rules. For better convenience, the proposed method is implemented within a web interface, which offers not only predictions but also map visualization and hotspots. It helps the user comprehend the concentration and distribution of crowd density in various locations. Our method is scalable and applicable in a practical environment as it does not require any costly big data or infrastructural resources. On the whole, our work shows the benefits of applying machine learning together with logical rules to urban mobility forecasting problems.