Location-Based Social Network (LBSN) check-in trajectory data are important for many practical applications like POI recommendation, advertising, and pandemic intervention. However, the high collection costs and ever-increasing privacy concerns prevent us from accessing large-scale LBSN trajectory data. The recent advances in synthetic data generation provide us with a new opportunity to achieve this, which utilizes generative AI to generate synthetic data that preserves the characteristics of real data while ensuring privacy protection. However, generating synthetic LBSN check-in trajectories remains challenging due to their spatially discrete, temporally irregular nature and the complex spatio-temporal patterns caused by sparse activities and uncertain human mobility. To address this challenge, we propose GeoGen, a two-stage coarse-to-fine framework for large-scale LBSN check-in trajectory generation. In the first stage, we reconstruct spatially continuous, temporally regular latent movement sequences from the original LBSN check-in trajectories and then design a Sparsity-aware Spatio-temporal Diffusion model (S^2TDiff) with an efficient denosing network to learn their underlying behavioral patterns. In the second stage, we design Coarse2FineNet, a Transformer-based Seq2Seq architecture equipped with a dynamic context fusion mechanism in the encoder and a multi-task hybrid-head decoder, which generates fine-grained LBSN trajectories based on coarse-grained latent movement sequences by modeling semantic relevance and behavioral uncertainty. Extensive experiments on four real-world datasets show that GeoGen excels state-of-the-art models for both fidelity and utility evaluation, e.g., it increases over 69% and 55% in distance and radius metrics on the FS-TKY dataset.

Spatiotemporal prediction plays a critical role in numerous real-world applications such as urban planning, transportation optimization, disaster response, and pandemic control. In recent years, researchers have made significant progress by developing advanced deep learning models for spatiotemporal prediction. However, most existing models are deterministic, i.e., predicting only the expected mean values without quantifying uncertainty, leading to potentially unreliable and inaccurate outcomes. While recent studies have introduced probabilistic models to quantify uncertainty, they typically focus on a single phenomenon (e.g., taxi, bike, crime, or traffic crashes), thereby neglecting the inherent correlations among heterogeneous urban phenomena. To address the research gap, we propose a novel Graph Neural Network with Uncertainty Quantification, termed UQGNN for multivariate spatiotemporal prediction. UQGNN introduces two key innovations: (i) an Interaction-aware Spatiotemporal Embedding Module that integrates a multivariate diffusion graph convolutional network and an interaction-aware temporal convolutional network to effectively capture complex spatial and temporal interaction patterns, and (ii) a multivariate probabilistic prediction module designed to estimate both expected mean values and associated uncertainties. Extensive experiments on four real-world multivariate spatiotemporal datasets from Shenzhen, New York City, and Chicago demonstrate that UQGNN consistently outperforms state-of-the-art baselines in both prediction accuracy and uncertainty quantification. For example, on the Shenzhen dataset, UQGNN achieves a 5% improvement in both prediction accuracy and uncertainty quantification.

The increasing frequency of extreme weather events, such as hurricanes, highlights the urgent need for efficient and equitable power system restoration. Many electricity providers make restoration decisions primarily based on the volume of power restoration requests from each region. However, our data-driven analysis reveals significant disparities in request submission volume, as disadvantaged communities tend to submit fewer restoration requests. This disparity makes the current restoration solution inequitable, leaving these communities vulnerable to extended power outages. To address this, we aim to propose an equity-aware power restoration strategy that balances both restoration efficiency and equity across communities. However, achieving this goal is challenging for two reasons: the difficulty of predicting repair durations under dataset heteroscedasticity, and the tendency of reinforcement learning agents to favor low-uncertainty actions, which potentially undermine equity. To overcome these challenges, we design a predict-then-optimize framework called EPOPR with two key components: (1) Equity-Conformalized Quantile Regression for uncertainty-aware repair duration prediction, and (2) Spatial-Temporal Attentional RL that adapts to varying uncertainty levels across regions for equitable decision-making. Experimental results show that our EPOPR effectively reduces the average power outage duration by 3.60% and decreases inequity between different communities by 14.19% compared to state-of-the-art baselines.

Order dispatch systems play a vital role in ride-hailing services, which directly influence operator revenue, driver profit, and passenger experience. Most existing work focuses on improving system efficiency in terms of operator revenue, which may cause a bad experience for both passengers and drivers. Hence, in this work, we aim to design a human-centered ride-hailing system by considering both passenger fairness and driver preference without compromising the overall system efficiency. However, it is nontrivial to achieve this target due to the potential conflicts between passenger fairness and driver preference since optimizing one may sacrifice the other. To address this challenge, we design HCRide, a Human-Centered Ride-hailing system based on a novel multi-agent reinforcement learning algorithm called Harmonization-oriented Actor-Bi-Critic (Habic), which includes three major components (i.e., a multi-agent competition mechanism, a dynamic Actor network, and a Bi-Critic network) to optimize system efficiency and passenger fairness with driver preference consideration. We extensively evaluate our HCRide using two real-world ride-hailing datasets from Shenzhen and New York City. Experimental results show our HCRide effectively improves system efficiency by 2.02%, fairness by 5.39%, and driver preference by 10.21% compared to state-of-the-art baselines.

With the worldwide mobility electrification initiative to reduce air pollution and energy security, more and more for-hire vehicles are being replaced with electric ones. A key difference between gas for-hire vehicles and electric for-hire vehicles (EFHV) is their energy replenishment mechanisms, i.e., refueling or charging, which is reflected in two aspects: (i) much longer charging processes vs. much shorter refueling processes and (ii) time-varying electricity prices vs. time-invariant gasoline prices during a day. The complicated charging issues (e.g., long charging time and dynamic charging pricing) potentially reduce the daily operation time and profits of EFHVs, and also cause overcrowded charging stations during some off-peak charging pricing periods. Motivated by a set of findings obtained from a data-driven investigation and field studies, in this paper, we design a fairness-aware vehicle displacement system calledFairMoveto jointly optimize the overall profit efficiency and profit fairness of EFHV drivers by considering both the passenger travel demand and vehicle charging demand. We first formulate the EFHV displacement problem as a Markov decision problem, and then we present a fairness-aware multi-agent actor-critic approach to tackle this problem. More importantly, we implement and evaluateFairMovewith real-world streaming data from the Chinese city Shenzhen, including GPS data and transaction data from over 20,100 EFHVs, coupled with the data of 123 charging stations, which constitute, to our knowledge, the largest EFHV network in the world. Extensive experimental results show that our fairness-awareFairMoveeffectively improves the profit efficiency and profit fairness of the EFHV fleet by 26.9% and 54.8%, respectively. It also improves the charging station utilization fairness by 38.4%.

In recent years, we have been witnessing a rapid prevalence of instant delivery services (e,g., UberEats, Instacart, and Eleme) due to their convenience and timeliness. A unique characteristic of instant delivery services is the concurrent dispatch mode, where (i) one courier usually simultaneously delivers multiple orders, especially during rush hours, and (ii) couriers can receive new orders when delivering existing orders. Most existing concurrent dispatch systems are efficiency-oriented, which means they usually dispatch a group of orders that have a similar delivery route to a courier. Although this strategy may achieve high overall efficiency, it also potentially causes a huge disparity of earnings between different couriers. To address the problem, in this paper, we design a Fairness-aware Concurrent dispatch system called FairCod, which aims to optimize the overall operation efficiency and individual fairness at the same time. Specifically, in FairCod, we design a Dynamic Advantage Actor-Critic algorithm with Fairness constrain (DA2CF). The basic idea is that it includes an Actor network to make dispatch decisions based on dynamic action space and a Critic network to evaluate the dispatch decisions from the fairness perspective. More importantly, we extensively evaluate our FairCod system based on one-month real-world data consisting of 36.38 million orders from 42,000 couriers collected by one of the largest instant delivery companies in China. Experimental results show that our FairCod improves courier fairness by 30.3% without sacrificing the overall system benefit compared to state-of-the-art baselines.

In this paper, we focus on the research of cross-corpus speech emotion recognition (SER), in which the training and testing speech signals in cross-corpus SER belong to dierent speech corpus. Due to this fact, mismatched feature distributions may exist between the training and testing speech feature sets degrading the performance of most originally well-performing SER methods. To deal with cross-corpus SER, we propose a novel domain adaptation (DA) method called joint distribution adaptive regression (JDAR). The basic idea of JDAR is to learn a regression matrix by jointly considering the marginal and conditional probability distribution between the training and testing speech signals and hence their feature distribution dierence can be alleviated in the subspace spanned by the learned regression matrix. To evaluate the proposed JDAR, we conduct extensive cross-corpus SER experiments on EmoDB, eNTERFACE, and CASIA speech databases. Experimental results show that the proposed JDAR achieves satisfactory performance and outperforms most of state-of-the-art subspace learning based DA methods.