Deep spatio-temporal neural networks are increasingly used in urban computing, impacting critical infrastructure such as public transport, emergency services, and traffic systems. While most methods focus on accuracy, fairness has become a key concern as biased predictions can disadvantage specific demographic or geographic groups, reinforcing inequalities. We propose FairDRL-ST, a disentangled representation learning framework for fair spatio-temporal prediction, with a focus on mobility demand forecasting. By combining adversarial and disentangled learning, our approach separates sensitive attributes and achieves fairness in an unsupervised manner with minimal performance loss. Experiments on real-world urban mobility datasets show that FairDRL-ST reduces fairness gaps while maintaining competitive predictive accuracy against state-of-the-art fairness-aware methods.1

Disentangled representation learning offers useful properties such as dimension reduction and interpretability, which are essential to modern deep learning approaches. Although deep learning techniques have been widely applied to spatio-temporal data mining, there has been little attention to further disentangle the latent features and understanding their contribution to the model performance, particularly their mutual information and correlation across features. In this study, we adopt two state-of-the-art disentangled representation learning methods and apply them to three large-scale public spatio-temporal datasets. To evaluate their performance, we propose an internal evaluation metric focusing on the degree of correlations among latent variables of the learned representations and the prediction performance of the downstream tasks. Empirical results show that our modified method can learn disentangled representations that achieve the same level of performance as existing state-of-the-art ST deep learning methods in a spatio-temporal sequence forecasting problem. Additionally, we find that our methods can be used to discover real-world spatial-temporal semantics to describe the variables in the learned representation.

Generative Adversarial Networks (GANs) have shown remarkable success in producing realistic-looking images in the computer vision area. Recently, GAN-based techniques are shown to be promising for spatio-temporal-based applications such as trajectory prediction, events generation, and time-series data imputation. While several reviews for GANs in computer vision have been presented, no one has considered addressing the practical applications and challenges relevant to spatio-temporal data. In this article, we have conducted a comprehensive review of the recent developments of GANs for spatio-temporal data. We summarise the application of popular GAN architectures for spatio-temporal data and the common practices for evaluating the performance of spatio-temporal applications with GANs. Finally, we point out future research directions to benefit researchers in this area.

Existing research on parking availability sensing mainly relies on extensive contextual and historical information. In practice, the availability of such information is a challenge as it requires continuous collection of sensory signals. In this study, we design an end-to-end transfer learning framework for parking availability sensing to predict parking occupancy in areas in which the parking data is insufficient to feed into data-hungry models. This framework overcomes two main challenges: 1) many real-world cases cannot provide enough data for most existing data-driven models, and 2) it is difficult to merge sensor data and heterogeneous contextual information due to the differing urban fabric and spatial characteristics. Our work adopts a widely-used concept, adversarial domain adaptation, to predict the parking occupancy in an area without abundant sensor data by leveraging data from other areas with similar features. In this paper, we utilise more than 35 million parking data records from sensors placed in two different cities, one a city centre and the other a coastal tourist town. We also utilise heterogeneous spatio-temporal contextual information from external resources, including weather and points of interest. We quantify the strength of our proposed framework in different cases and compare it to the existing data-driven approaches. The results show that the proposed framework is comparable to existing state-of-the-art methods and also provide some valuable insights on parking availability prediction.

The prediction of flight delays plays a significantly important role for airlines and travellers because flight delays cause not only tremendous economic loss but also potential security risks. In this work, we aim to integrate multiple data sources to predict the departure delay of a scheduled flight. Different from previous work, we are the first group, to our best knowledge, to take advantage of airport situational awareness map, which is defined as airport traffic complexity (ATC), and combine the proposed ATC factors with weather conditions and light information. Features engineering methods and most state-of-the-art machine learning algorithms are applied to a large real-world data sources. We reveal a couple of factors at the airport which has a significant impact on flight departure delay time. The prediction results show that the proposed factors are the main reasons behind the flight delays. Using our proposed framework, an improvement in accuracy for flight departure delay prediction is obtained.