In recent years, utility optimization and fairness have emerged as key objectives in spatial-temporal resource allocation systems, such as ridesharing and food delivery, attracting significant research interest due to their often conflicting nature. Systems focused solely on optimizing utility can create disparities, such as disparate earnings among rideshare drivers, leading to ethical and economic issues. Conversely, focusing solely on fairness can paradoxically reduce overall utility, resulting in a scenario where no one truly benefits. To address this, many Reinforcement Learning (RL)-based approaches have been proposed due to its capacity to generate dynamic, non-myopic allocation plans. Typically, RL-based approaches balance utility and fairness by constructing direct trade-off functions between them. However, this paper argues that in dynamic situations, the scale of the objectives may differ significantly, causing RL to overemphasize one objective at the expense of the other. To effectively address this issue, we propose formulating the challenge of balancing utility and fairness in spatial-temporal resource allocation as a Constrained Markov Decision Process. Here, fairness is treated as a constraint while utility optimization serves as the standard reward. Building on this foundation, we introduce Dynamic Budgeted Proximal Policy Optimization (DB-PPO). We demonstrate that DB-PPO outperforms existing approaches in fairness-oriented spatial-temporal resource allocations using two real-world publicly available datasets.

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

Abstract With the introduction of machine learning in high stakes decision-making, ensuring algorithmic fairness has become an increasingly important task. To this end, many mathematical definitions of fairness have been proposed, and a variety of optimisation techniques have been developed, all designed to maximise a given notion of fairness. Fair solutions, however, tend to rely on the quality of training data, and can be highly sensitive to noise. Recent studies have shown that robustness of many such fairness strategies—i.e., their ability to perform well on unseen data—is not a given and requires careful consideration. To address this challenge, we propose robustness ratio , which is a novel criterion to measure the robustness of diverse fairness optimisation strategies. We support our analysis with multiple extensive experiments on five benchmark fairness data sets, using three prominent fairness strategies, in view of four of the most popular definitions of fairness. Our experiments show that while fairness methods that rely on threshold optimisation (post-processing) mostly outperform other techniques, they are acutely sensitive to noise. This is in contrast to two other methods—correlation remover (pre-processing) and exponentiated gradient descent (in-processing)—which become increasingly fairer as the random noise injected into the data becomes larger. Our findings offer a comprehensive overview of fairness strategies that proves invaluable when tasked with choosing the most suitable method for the task at hand. To the best of our knowledge, we are the first to quantitatively evaluate the robustness of fairness optimisation strategies.

Accuracy and timeliness are indeed often conflicting goals in prediction tasks.Premature predictions may yield a higher rate of false alarms, whereas delaying predictions to gather more information can render them too late to be useful.In applications such as wildfires, crimes, and traffic jams, timely forecasting are vital for safeguarding human life and property.Consequently, finding a balance between accuracy and timeliness is crucial.In this paper, we propose an early spatio-temporal forecasting model based on Multi-Objective reinforcement learning that can either implement an optimal policy given a preference or infer the preference based on a small number of samples.The model addresses two primary challenges: 1) enhancing the accuracy of early forecasting and 2) providing the optimal policy for determining the most suitable prediction time for each area.Our method demonstrates superior performance on three large-scale real-world datasets, surpassing existing methods in early spatio-temporal forecasting tasks.

Abstract New roads are being constructed all the time. However, the capabilities of previous deep forecasting models to generalize to new roads not seen in the training data (unseen roads) are rarely explored. In this paper, we introduce a novel setup called a spatio-temporal split to evaluate the models’ capabilities to generalize to unseen roads. In this setup, the models are trained on data from a sample of roads, but tested on roads not seen in the training data. Moreover, we also present a novel framework called Spatial Contrastive Pre-Training (SCPT) where we introduce a spatial encoder module to extract latent features from unseen roads during inference time. This spatial encoder is pre-trained using contrastive learning. During inference, the spatial encoder only requires two days of traffic data on the new roads and does not require any re-training. We also show that the output from the spatial encoder can be used effectively to infer latent node embeddings on unseen roads during inference time. The SCPT framework also incorporates a new layer, named the spatially gated addition layer, to effectively combine the latent features from the output of the spatial encoder to existing backbones. Additionally, since there is limited data on the unseen roads, we argue that it is better to decouple traffic signals to trivial-to-capture periodic signals and difficult-to-capture Markovian signals, and for the spatial encoder to only learn the Markovian signals. Finally, we empirically evaluated SCPT using the ST split setup on four real-world datasets. The results showed that adding SCPT to a backbone consistently improves forecasting performance on unseen roads. More importantly, the improvements are greater when forecasting further into the future. The codes are available on GitHub: https://github.com/cruiseresearchgroup/forecasting-on-new-roads .

Given the increasing importance of predicting spatiotemporal events such as wildfire, crime, and traffic congestion, existing methods are faced with the challenge of balancing timeliness and accuracy. Late predictions may result in tremendous economic costs and human life loss, while inaccurate predictions are likely to cause unnecessary public resources and social anxiety. Therefore, balancing accuracy and timeliness is essential in general spatiotemporal event prediction problems. In this paper, we propose an Early Spatiotemporal Graph Convolutional Network (ESTGCN)1to adaptively determine the optimal prediction time, which makes a tradeoff between prediction accuracy and timeliness and addresses two major questions: 1) How can we determine optimal prediction time points for different areas, taking into account their unique characteristics and conditions? 2) How can we minimize the propagation of prediction errors throughout the forecast timeline? Extensive experiments on two large-scale real-world datasets demonstrate that our proposed approaches can give an optimal prediction time in advance for each area and outperform all baselines in early spatiotemporal prediction tasks.

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 parking recommendation solutions mainly focus on finding and suggesting parking spaces based on the unoccupied options only. However, there are other factors associated with parking spaces that can influence someone’s choice of parking such as fare, parking rule, walking distance to destination, travel time, likelihood to be unoccupied at a given time. More importantly, these factors may change over time and conflict with each other which makes the recommendations produced by current parking recommender systems ineffective. In this paper, we propose a novel problem called multi-objective parking recommendation. We present a solution by designing a multi-objective parking recommendation engine called MoParkeR that considers various conflicting factors together. Specifically, we utilise a non-dominated sorting technique to calculate a set of Pareto-optimal solutions, consisting of recommended trade-off parking spots. We conduct extensive experiments using two real-world datasets to show the applicability of our multi-objective recommendation methodology.

Network alignment is useful for multiple applications that require increasingly large graphs to be processed. Existing research approaches this as an optimization problem or computes the similarity based on node representations. However, the process of aligning every pair of nodes between relatively large networks is time-consuming and resource-intensive. In this paper, we propose a framework, called G-CREWE (Graph CompREssion With Embedding) to solve the network alignment problem. G-CREWE uses node embeddings to align the networks on two levels of resolution, a fine resolution given by the original network and a coarse resolution given by a compressed version, to achieve an efficient and effective network alignment. The framework first extracts node features and learns the node embedding via a Graph Convolutional Network (GCN). Then, node embedding helps to guide the process of graph compression and finally improve the alignment performance. As part of G-CREWE, we also propose a new compression mechanism called MERGE (Minimum DEgRee NeiGhbors ComprEssion) to reduce the size of the input networks while preserving the consistency in their topological structure. Experiments on all real networks show that our method is more than twice as fast as the most competitive existing methods while maintaining high accuracy.

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.