GODDAG: Generating Origin-Destination Flow for New Cities Via Domain Adversarial Training

Abstract

Origin-destination (OD) flow data, which reflects population mobility patterns in the city, is very important in many urban applications, such as urban planning and public resource allocation, etc. However, due to the high cost of money and time during device deployment and social surveys, it is challenging to obtain OD flow data, especially in developing cities and emerging cities where historical OD flow data is scarce. Therefore, it is necessary to investigate a method that can generate OD flow in cities where OD flow data are not available. The research on modeling population mobility in the city has a long history. Traditional gravity models, etc., are too simple to model the complex population mobility; recently proposed machine learning models and deep learning models are not applicable in cities where data are scarce because the parameters must be fitted with abundant data. To solve the problem of difficult access to OD flow data, we propose a method to learn mobility knowledge with ample data in the source city and generate OD flow data in new cities named <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GODDAG</b> ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>G</u></b> enerating <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>O</u></b> rigin- <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>D</u></b> estination Flow via <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>D</u></b> omain <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>A</u></b> dversarial Trainin <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>g</u></b> ). Our proposed method consists of two parts, one is a GNN (graph neural networks) based mobility model generating OD flow between every two regions based on regional attributes such as census and POI distribution, and the other is a domain adversarial training strategy to make the model have better transfer ability between different cities. Extensive experiments are conducted on two real-world datasets to prove the validity of our methods.