The deployment of sensors enables data-driven urban management, but necessitates inductive spatio-temporal kriging to infer unmonitored areas. Existing methods impute these unknown observations by smoothing temporal features based on spatial dependencies, overlooking the decoupling ofinherent propertiesanddynamic correlationsin message passing. In particular, the inherent properties reveal non-transitive signals, and current coupled aggregation leads to inaccurate results. To this end, we proposeTempoRAl deCoupledKriging, named TRACK, to decouple two factors with the help of node-specific inherency. Specifically, we first construct a node-specific profile to represent its inherency including geographical and periodic features, which is subsequently transformed into decoupling prompts. Secondly, the coupled temporal features are separated through querying each prompt embedding, facilitating precise temporal aggregation for inherent properties and spatial aggregation for dynamic correlations. Finally, a multi-task training strategy is further adopted to mimic the inductive scenarios during testing. We evaluate TRACK on four real-world datasets spanning urban traffic and air quality prediction tasks. TRACK achieves state-of-the-art performance, with average improvements of 3.10% in MAE and 4.45% in RMSE over strong baselines. Moreover, we further demonstrated its robust generalization in a challenging cross-city inductive setting. Code is available athttps://github.com/JeremyChou28/TRACK.

Graph anomaly detection (GAD) is a critical task in graph machine learning, with the primary objective of identifying anomalous nodes that deviate significantly from the majority. This task is widely applied in various real-world scenarios, including fraud detection and social network analysis. However, existing GAD methods still face two major challenges: (1) They are often limited to detecting anomalies in single-type interaction graphs and struggle with multiple interaction types in multiplex heterogeneous graphs. (2) In unsupervised scenarios, selecting appropriate anomaly score thresholds remains a significant challenge for accurate anomaly detection. To address the above challenges, we propose a novel Unsupervised Multiplex Graph Anomaly Detection method, named UMGAD. We first learn multi-relational correlations among nodes in multiplex heterogeneous graphs and capture anomaly information during node attribute and structure reconstruction through graph-masked autoencoder (GMAE). Then, to further extract abnormal information, we generate attribute-level and subgraph-level augmented-view graphs, respectively, and perform attribute and structure reconstruction through GMAE. Finally, we learn to optimize node attributes and structural features through contrastive learning between original-view and augmented-view graphs to improve the model's ability to capture anomalies. Meanwhile, we propose a new anomaly score threshold selection strategy, which allows the model to be independent of ground truth information in real unsupervised scenarios. Extensive experiments on six datasets show that our UMGAD significantly outperforms state-of-the-art methods, achieving average improvements of 12.25% in AUC and 11.29% in Macro-F1 across all datasets. The source code of our model is available at https://github.com/lx970414/UMGAD.

Traffic forecasting is crucial for intelligent transportation systems (ITS) , aiding in efficient resource allocation and effective traffic control. However, its effectiveness often relies heavily on abundant traffic data, while many cities lack sufficient data due to limited device support, posing a significant challenge for traffic forecasting. Recognizing this challenge, we have made a noteworthy observation: traffic patterns exhibit similarities across diverse cities. Building on this key insight, we propose a solution for the cross-city few-shot traffic forecasting problem called Multi-scale Traffic Pattern Bank (MTPB) . Primarily, MTPB initiates its learning process by leveraging data-rich source cities, effectively acquiring comprehensive traffic knowledge through a spatial-temporal-aware pre-training process. Subsequently, the framework employs advanced clustering techniques to systematically generate a multi-scale traffic pattern bank derived from the learned knowledge. Next, the traffic data of the data-scarce target city could query the traffic pattern bank, facilitating the aggregation of meta-knowledge. This meta-knowledge, in turn, assumes a pivotal role as a robust guide in subsequent processes involving graph reconstruction and forecasting. Empirical assessments conducted on real-world traffic datasets affirm the superior performance of MTPB, surpassing existing methods across various categories and exhibiting numerous attributes conducive to the advancement of cross-city few-shot forecasting methodologies. The code is available in https://github.com/zhyliu00/MTPB .

Due to detector malfunctions and communication failures, missing data is ubiquitous during the collection of traffic data. Therefore, it is of vital importance to impute the missing values to facilitate data analysis and decision-making for Intelligent Transportation System (ITS) . However, existing imputation methods generally perform zero pre-filling techniques to initialize missing values, introducing inevitable noise. Moreover, we observe prevalent over-smoothed interpolations, falling short in revealing the intrinsic spatio-temporal correlations of incomplete traffic data. To this end, we propose Mask-Aware Graph Imputation Network (MagiNet) . Our method designs an adaptive mask spatio-temporal encoder to learn the latent representations of incomplete data, eliminating the reliance on pre-filling missing values. Furthermore, we devise a spatio-temporal decoder that stacks multiple blocks to capture the inherent spatial and temporal dependencies within incomplete traffic data, alleviating over-smoothed imputation. Extensive experiments demonstrate that our method outperforms state-of-the-art imputation methods on five real-world traffic datasets, yielding an average improvement of 4.31% in RMSE and 3.72% in MAPE under Missing Completely at Random (MCAR) pattern. Code is available at https://github.com/JeremyChou28/MagiNet .

Missing values are prevalent in multivariate time series, compromising the integrity of analyses and degrading the performance of downstream tasks. Consequently, research has focused on multivariate time series imputation, aiming to accurately impute the missing values based on available observations. A key research question is how to ensure imputation consistency, i.e., intra-consistency between observed and imputed values, and inter-consistency between adjacent windows after imputation. However, previous methods rely solely on the inductive bias of the imputation targets to guide the learning process, ignoring imputation consistency and ultimately resulting in poor performance. Diffusion models, known for their powerful generative abilities, prefer to generate consistent results based on available observations. Therefore, we propose a conditional diffusion model for Multivariate Time Series Consistent Imputation (MTSCI). Specifically, MTSCI employs a contrastive complementary mask to generate dual views during the forward noising process. Then, the intra contrastive loss is calculated to ensure intra-consistency between the imputed and observed values. Meanwhile, MTSCI utilizes a mixup mechanism to incorporate conditional information from adjacent windows during the denoising process, facilitating the inter-consistency between imputed samples. Extensive experiments on multiple real-world datasets demonstrate that our method achieves the state-of-the-art performance on multivariate time series imputation task under different missing scenarios. Code is available at https://github.com/JeremyChou28/MTSCI.

Time series data has been demonstrated to be crucial in various research fields. The management of large quantities of time series data presents challenges in terms of deep learning tasks, particularly for training a deep neural network. Recently, a technique named Dataset Condensation has emerged as a solution to this problem. This technique generates a smaller synthetic dataset that has comparable performance to the full real dataset in downstream tasks such as classification. However, previous methods are primarily designed for image and graph datasets, and directly adapting them to the time series dataset leads to suboptimal performance due to their inability to effectively leverage the rich information inherent in time series data, particularly in the frequency domain. In this paper, we propose a novel framework named Dataset Condensation for Time Series Classification via Dual Domain Matching (CondTSC) which focuses on the time series classification dataset condensation task. Different from previous methods, our proposed framework aims to generate a condensed dataset that matches the surrogate objectives in both the time and frequency domains. Specifically, CondTSC incorporates multi-view data augmentation, dual domain training, and dual surrogate objectives to enhance the dataset condensation process in the time and frequency domains. Through extensive experiments, we demonstrate the effectiveness of our proposed framework, which outperforms other baselines and learns a condensed synthetic dataset that exhibits desirable characteristics such as conforming to the distribution of the original data.

With the increasing demands of training graph neural networks (GNNs) on large-scale graphs, graph data condensation has emerged as a critical technique to relieve the storage and time costs during the training phase. It aims to condense the original large-scale graph to a much smaller synthetic graph while preserving the essential information necessary for efficiently training a downstream GNN. However, existing methods concentrate either on optimizing node features exclusively or endeavor to independently learn node features and the graph structure generator. They could not explicitly leverage the information of the original graph structure and failed to construct an interpretable graph structure for the synthetic dataset. To address these issues, we introduce a novel framework named Graph Data Condensation via Self-expressive Graph Structure Reconstruction (GCSR). Our method stands out by (1) explicitly incorporating the original graph structure into the condensing process and (2) capturing the nuanced interdependencies between the condensed nodes by reconstructing an interpretable self-expressive graph structure. Extensive experiments and comprehensive analysis validate the efficacy of the proposed method across diverse GNN models and datasets. Our code is available at https://github.com/zclzcl0223/GCSR.

Traffic forecasting is a critical service in Intelligent Transportation Systems (ITS). Utilizing deep models to tackle this task relies heavily on data from traffic sensors or vehicle devices, while some cities might lack device support and thus have few available data. So, it is necessary to learn from data-rich cities and transfer the knowledge to data-scarce cities in order to improve the performance of traffic forecasting. To address this problem, we propose a cross-city few-shot traffic forecasting framework via Traffic Pattern Bank (TPB) due to that the traffic patterns are similar across cities. TPB utilizes a pre-trained traffic patch encoder to project raw traffic data from data-rich cities into high-dimensional space, from which a traffic pattern bank is generated through clustering. Then, the traffic data of the data-scarce city could query the traffic pattern bank and explicit relations between them are constructed. The metaknowledge is aggregated based on these relations and an adjacency matrix is constructed to guide a downstream spatial-temporal model in forecasting future traffic. The frequently used meta-training framework Reptile is adapted to find a better initial parameter for the learnable modules. Experiments on real-world traffic datasets show that TPB outperforms existing methods and demonstrates the effectiveness of our approach in cross-city few-shot traffic forecasting.

Traffic prediction is a crucial topic because of its broad scope of applications in the transportation domain. Though recent studies have achieved promising results, most of them cannot adequately deal with positions with no historical data, which is common due to limited resources in real life. Apart from this, the lack of uncertainty measurements also makes current models unable to manage risks, especially for the downstream tasks involving decision-making. Inspired by the previous inductive graph neural network, we proposed an uncertainty-aware framework to 1) extend prediction to locations with no historical records and significantly extend spatial coverage of prediction while reducing sensor deployment and 2) generate probabilistic prediction with uncertainty quantification to help the risk management. The experiment results show that our method achieved promising results on prediction tasks, and the uncertainty quantification gives consistent results that highly correlate with the locations with and without historical data. We also show that our model could help support sensor deployment tasks in the transportation field to achieve higher accuracy with a limited sensor deployment budget.

Heterogeneous graph neural networks have gained great popularity in tackling various network analysis tasks on heterogeneous network data. However, most existing works mainly focus on general heterogeneous networks, and assume that there is only one type of edge between two nodes, while ignoring the multiplex characteristics between multi-typed nodes in multiplex heterogeneous networks and the different importance of multiplex structures among nodes for node embedding. In addition, the over-smoothing issue of graph neural networks limits existing models to only capturing local structure signals but hardly learning the global relevant information of the network. To tackle these challenges, this work proposes a model called Behavior Pattern based Heterogeneous Graph Neural Network (BPHGNN) for multiplex heterogeneous network embedding. Specifically, BPHGNN can collaboratively learn node representations across different multiplex structures among nodes with adaptive importance learning from local and global perspectives in multiplex heterogeneous networks through depth behavior pattern aggregation and breadth behavior pattern aggregation. Extensive experiments on six real-world networks with various network analytical tasks demonstrate the significant superiority of BPHGNN against state-of-the-art approaches in terms of various evaluation metrics.

Traffic simulation provides interactive data for the optimization of traffic control policies. However, existing traffic simulators are limited by their lack of scalability and shortage in input data, which prevents them from generating interactive data from traffic simulation in the scenarios of real large-scale city road networks.

People have been working long to tackle the traffic congestion problem. Among the different measures, traffic tolling has been recognized as an effective way to mitigate citywide congestion. However, traditional tolling methods can not deal with the dynamic traffic flow in cities. Meanwhile, thanks to the development of traffic sensing technology, how to set appropriate dynamic tolling according to real time traffic observations has attracted research attention in recent years.

Understanding city-wide traffic problems may benefit many downstream applications, such as city planning and public transportation development. One key step to understand traffic is to reveal how many people travel from one location to another during one period (we call TOD, short for temporal origin-destination). With TOD, we can rebuild the city-wide traffic by simulating the volume and speed on each road segment.Frequently used mobility data, e.g., GPS trajectories, surveillance cameras, can only cover a subset of vehicles or selected regions of the city. Hence, we propose to use pervasive speed data to recover TOD, and use other mobility data as auxiliary data. To the best of our knowledge, we are the first to work on this challenging problem. It is highly challenging because the speed is generated from a complex process from TOD, and there exists multiple TOD distributions that may generate similar city-wide road speed observations. We propose a new method that models the complex process via separate modules and takes auxiliary data to eliminate infeasible solutions. Extensive experiments on synthetic and real datasets have shown the superior performance of our model over baselines.

The 1st Workshop on City Brain Research examines the current challenges and recent breakthroughs related to intelligent urban transportation. The workshop will be organized in a novel form --- offering debates on three main components involved in the transportation policy development cycle: data collection, policy learning, and the effects on human behavior. The organizers intend to invite speakers and attendees from different backgrounds, ranging from computer science, transportation, to urban planning. The final outcomes include live discussions of the three consistent topics, a comprehensive annual report summarizing current practices and future directions, and a detailed tutorial on the workshop day.

The heavy traffic congestion problem has always been a concern for modern cities. To alleviate traffic congestion, researchers use reinforcement learning (RL) to develop better traffic signal control (TSC) algorithms in recent years. However, most RL models are trained and tested in the same traffic flow environment, which results in a serious overfitting problem. Since the traffic flow environment in the real world keeps varying, these models can hardly be applied due to the lack of generalization ability. Besides, the limited number of accessible traffic flow data brings extra difficulty in testing the generalization ability of the models. In this paper, we design a novel traffic flow generator based on Wasserstein generative adversarial network to generate sufficient diverse and quality traffic flows and use them to build proper training and testing environments. Then we propose a meta-RL TSC framework GeneraLight to improve the generalization ability of TSC models. GeneraLight boosts the generalization performance by combining the idea of flow clustering and model-agnostic meta-learning. We conduct extensive experiments on multiple real-world datasets to show the superior performance of GeneraLight on generalizing to different traffic flows.

Traffic simulations can help to explore novel and efficient transportation solutions that overcome traffic problems such as traffic jams and road planning. Traditional traffic simulators usually leverage a car-following model to simulate the vehicle's behavior in the real-world traffic environment. However, these calibrated simplified physical models often fail to accurately predict the pattern of vehicle's movement in complicated real-world traffic environment. Considering the complexity and non-linearity of the real-world traffic, this paper unprecedentedly treat the problem of traffic simulation as a learning problem, and proposes learning to simulate (L2S) vehicle trajectory. We use the generative adversarial imitation learning framework to estimate the policy that provides sequential decisions for the vehicle given real-world demonstrations. The experiment on real-world traffic data shows the superior performance in simulating vehicle trajectories of our method compared to traditional traffic simulation approaches.

Cooperation among the traffic signals enables vehicles to move through intersections more quickly. Conventional transportation approaches implement cooperation by pre-calculating the offsets between two intersections. Such pre-calculated offsets are not suitable for dynamic traffic environments. To enable cooperation of traffic signals, in this paper, we propose a model, CoLight, which uses graph attentional networks to facilitate communication. Specifically, for a target intersection in a network, CoLight can not only incorporate the temporal and spatial influences of neighboring intersections to the target intersection, but also build up index-free modeling of neighboring intersections. To the best of our knowledge, we are the first to use graph attentional networks in the setting of reinforcement learning for traffic signal control and to conduct experiments on the large-scale road network with hundreds of traffic signals. In experiments, we demonstrate that by learning the communication, the proposed model can achieve superior performance against the state-of-the-art methods.

Reinforcement learning (RL) has recently become a promising approach in various decision-making tasks. Among them, traffic signal control is the one where RL makes a great breakthrough. However, these methods always suffer from the prominent exploration problem and even fail to converge. To resolve this issue, we make an analogy between agents and humans. Agents can learn from demonstrations generated by traditional traffic signal control methods, in the similar way as people master a skill from expert knowledge. Therefore, we propose DemoLight, for the first time, to leverage demonstrations collected from classic methods to accelerate learning. Based on the state-of-the-art deep RL method Advantage Actor-Critic (A2C), training with demos are carried out for both the actor and the critic and reinforcement learning is followed for further improvement. Results under real-world datasets show that DemoLight enables a more efficient exploration and outperforms existing baselines with faster convergence and better performance.

Increasingly available city data and advanced learning techniques have empowered people to improve the efficiency of our city functions. Among them, improving urban transportation efficiency is one of the most prominent topics. Recent studies have proposed to use reinforcement learning (RL) for traffic signal control. Different from traditional transportation approaches which rely heavily on prior knowledge, RL can learn directly from the feedback. However, without a careful model design, existing RL methods typically take a long time to converge and the learned models may fail to adapt to new scenarios. For example, a model trained well for morning traffic may not work for the afternoon traffic because the traffic flow could be reversed, resulting in very different state representation. In this paper, we propose a novel design called FRAP, which is based on the intuitive principle of phase competition in traffic signal control: when two traffic signals conflict, priority should be given to one with larger traffic movement (i.e., higher demand). Through the phase competition modeling, our model achieves invariance to symmetrical cases such as flipping and rotation in traffic flow. By conducting comprehensive experiments, we demonstrate that our model finds better solutions than existing RL methods in the complicated all-phase selection problem, converges much faster during training, and achieves superior generalizability for different road structures and traffic conditions.

Traffic signal control is essential for transportation efficiency in road networks. It has been a challenging problem because of the complexity in traffic dynamics. Conventional transportation research suffers from the incompetency to adapt to dynamic traffic situations. Recent studies propose to use reinforcement learning (RL) to search for more efficient traffic signal plans. However, most existing RL-based studies design the key elements - reward and state - in a heuristic way. This results in highly sensitive performances and a long learning process. To avoid the heuristic design of RL elements, we propose to connect RL with recent studies in transportation research. Our method is inspired by the state-of-the-art method max pressure (MP) in the transportation field. The reward design of our method is well supported by the theory in MP, which can be proved to be maximizing the throughput of the traffic network, i.e., minimizing the overall network travel time. We also show that our concise state representation can fully support the optimization of the proposed reward function. Through comprehensive experiments, we demonstrate that our method outperforms both conventional transportation approaches and existing learning-based methods.

The intelligent traffic light control is critical for an efficient transportation system. While existing traffic lights are mostly operated by hand-crafted rules, an intelligent traffic light control system should be dynamically adjusted to real-time traffic. There is an emerging trend of using deep reinforcement learning technique for traffic light control and recent studies have shown promising results. However, existing studies have not yet tested the methods on the real-world traffic data and they only focus on studying the rewards without interpreting the policies. In this paper, we propose a more effective deep reinforcement learning model for traffic light control. We test our method on a large-scale real traffic dataset obtained from surveillance cameras. We also show some interesting case studies of policies learned from the real data.

In this paper, we propose a novel Deep Reinforcement Learning framework for news recommendation. Online personalized news recommendation is a highly challenging problem due to the dynamic nature of news features and user preferences. Although some online recommendation models have been proposed to address the dynamic nature of news recommendation, these methods have three major issues. First, they only try to model current reward (e.g., Click Through Rate). Second, very few studies consider to use user feedback other than click / no click labels (e.g., how frequent user returns) to help improve recommendation. Third, these methods tend to keep recommending similar news to users, which may cause users to get bored. Therefore, to address the aforementioned challenges, we propose a Deep Q-Learning based recommendation framework, which can model future reward explicitly. We further consider user return pattern as a supplement to click / no click label in order to capture more user feedback information. In addition, an effective exploration strategy is incorporated to find new attractive news for users. Extensive experiments are conducted on the offline dataset and online production environment of a commercial news recommendation application and have shown the superior performance of our methods.

Hydraulic fracturing (or "fracking") is a revolutionary well stimulation technique for shale gas extraction, but has spawned controversy in environmental contamination. If methane from gas wells leaks extensively, this greenhouse gas can impact drinking water wells and enhance global warming. Our work is motivated by this heated debate on environmental issue and focuses on general data analytical techniques to detect anomalous spatial data samples (e.g., water samples related to potential leakages). Specifically, we propose a spatial outlier detection method based on contextual neighbors. Different from existing work, our approach utilizes both spatial attributes and non-spatial contextual attributes to define neighbors. We further use robust metric learning to combine different contextual attributes in order to find meaningful neighbors. Our technique can be applied to any spatial dataset. Extensive experimental results on five real-world datasets demonstrate the effectiveness of our approach. We also show some interesting case studies, including one case linking to leakage of a gas well.

Causality analysis, beyond “mere” correlations, has become increasingly important for scientific discoveries and policy decisions. Many of these real-world applications involve time series data. A key observation is that the causality between time series could vary significantly over time. For example, a rain could cause severe traffic jams during the rush hours, but has little impact on the traffic at midnight. However, previous studies mostly look at the whole time series when determining the causal relationship between them. Instead, we propose to detect the partial time intervals with causality. As it is time consuming to enumerate all time intervals and test causality for each interval, we further propose an efficient algorithm that can avoid unnecessary computations based on the bounds of F-test in the Granger causality test. We use both synthetic datasets and real datasets to demonstrate the efficiency of our pruning techniques and that our method can effectively discover interesting causal intervals in the time series data.