Urban Mobility Analytics: Understanding, Inference and Forecasting

Abstract

Transport systems are the backbones of social and economic activities, which promote industry development and accelerate the process of urbanization. However, the contradiction between the pursuit of travel quality and unbalanced/inadequate development needs the rational construction and operation of transport systems. Owing to the evolution of a massive amount of multi-source data from transport systems, urban mobility analytics, including understanding, inference, and forecasting, support the management and control of transport, which attracts great attention in the long term and becomes more essential in smart transport research. In this thesis, we focus on inferring passenger demographics and predicting passenger demand by understanding travel patterns based on deep spatial-temporal learning algorithms. We first review the latest state-of-the-art deep learning methods for traffic understanding and attributes inference, traffic forecasting, and demand forecasting to form an overview of the current research progress. Second, we introduce the study public transport dataset collected from the Greater Sydney area and analyze the distributions and similarities of multiple transport modes. Third, we study the investigation of spatial and temporal features in order to infer traveler attributes by proposing a deep-based network with two modules (i.e., a Product-based Spatial-Temporal Module and an Auto-Encoder-based Compression Module). In addition, we study providing confidence interval-based passenger demand forecasting by proposing Probabilistic Graph Convolution Model to help relevant authorities and institutions to better accommodate demand uncertainty/variability. Then, to explore the relations in multimodal transport to boost the demand prediction performance, we propose two deep-based networks for knowledge adaptation between different transport modes by data sharing and model sharing, respectively. Finally, we provide promising directions for future works and conclude the thesis