Understanding and Modeling Urban Mobility Dynamics via Disentangled Representation Learning

Understanding the underlying patterns of the urban mobility dynamics is essential for both the traffic state estimation and management of urban facilities and services. Due to the coupling relationship of generative factors in spatial-temporal domain, it is challenging to model the citywide traffic dynamics under a structural pattern of critical features such as hours of days, days of weeks and weather conditions. To address this challenge, this article develops a disentangled representation learning framework to learn an interpretable factorized representation of the independent data generative factors. In order to make full use of the knowledge on generative factors, this article proposes spatial-temporal generative adversarial network (ST-GAN) to assign the generative factors of traffic flow to the feature vector in latent space and reconstructs the high-dimensional citywide traffic flow from the given factors. With the help of the disentangled representations, the decomposed feature vector in latent space discloses the relationship between underlying patterns and citywide traffic dynamics. Several comprehensively experiments show that ST-GAN not only effectively improves the prediction accuracy but also promisingly characterize structural properties of the traffic evolution process.

Automated summary

Understanding the underlying patterns of urban mobility dynamics is crucial for estimating traffic state and managing urban facilities and services. This article proposes a disentangled representation learning framework and a spatial-temporal generative adversarial network (ST-GAN) to model the citywide traffic dynamics and reconstruct the high-dimensional traffic flow. Experimental results show that ST-GAN not only improves prediction accuracy but also characterizes the structural properties of the traffic evolution process.