Spatio-temporal data proliferates in numerous real-world domains, such as transportation, weather, and energy. Spatio-temporal deep learning models aims to utilize useful patterns in such data to support tasks like prediction, imputation, and anomaly detection. However, previousone-to-onedeep learning models designed for specific tasks typically require separate training for each use case, leading to increased computational and storage costs. To address this issue,one-to-manyspatio-temporal foundation models have emerged, offering a unified framework capable of solving multiple spatio-temporal tasks. These foundation models achieve remarkable success by learning general knowledge with spatio-temporal data or transferring the general capabilities of pre-trained language models. While previous surveys have explored spatio-temporal data and methodologies separately, they have ignored a comprehensive examination of how foundation models are designed, selected, pre-trained, and adapted. As a result, the overall pipeline for spatio-temporal foundation models remains unclear. To bridge this gap, we innovatively provide an up-to-date review of previous spatio-temporal foundation models from the pipeline perspective. The pipeline begins with an introduction to different types of spatio-temporal data, followed by details of data preprocessing and embedding techniques. The pipeline then presents a novel data property taxonomy to divide existing methods according to data sources and dependencies, providing efficient and effective model design and selection for researchers. On this basis, we further illustrate the training objectives of primitive models, as well as the adaptation techniques of transferred models. Overall, our survey provides a clear and structured pipeline to understand the connection between core elements of spatio-temporal foundation models while guiding researchers to get started quickly. Additionally, we introduce emerging opportunities such as multi-objective training in the field of spatio-temporal foundation models, providing valuable insights for researchers and practitioners.

Velocity control in autonomous driving is an emerging technology that has achieved rapid progress over the last decade. However, existing velocity control studies ignore cascading disturbances in multi-lane scenarios and usually ignore the negative impact caused by harsh velocity decisions. To address these issues, we propose a reinforcement learning-based framework, called RISE (contRol velocIty for autonomouSvEhicle) to make velocity decisions for an autonomous vehicle in multi-lane traffic scenarios. To detect latent disturbances in the traffic flow, we propose a novel state encoder to learn the spatio-temporal correlation between different vehicles based on a well-designed impact graph. Afterward, we introduce an actor-critic paradigm to make velocity decisions with the aid of a hybrid reward function considering four optimization objectives: safety, efficiency, comfort, and impact. In particular, the impact term can penalize the harsh decisions of the autonomous vehicle, thus encouraging it to reduce the negative impacts on traffic flow. Further, we propose an improved RISE (RISE++) framework that incorporates a motion prediction model to augment state features for reasonable decisions, a modification of the reward function for energy efficiency, and a multi-worker paradigm for training efficiency. Extensive experiments offer evidence that the proposed framework can advance the state of the art in terms of effectiveness and efficiency.

Autonomous driving is an emerging technology that has developed rapidly over the last decade, with decision-making remaining a critical challenge, particularly due to its significant role in traffic congestion. In this paper, we propose a novel perception-and-decision framework, calledHEAD, which consists of an enHanced pErception module and a mAneuverDecision module to address this challenge. In the enhanced perception module, a graph-based state prediction model with a strategy of phantom vehicle construction is proposed to address incomplete vehicle features and predict future states in parallel. Then in the maneuver decision module, a deep reinforcement learning-based model is designed to learn a driving policy based on a parameterized action Markov decision process. A hybrid reward function takes into account aspects of safety, efficiency, comfort, and impact to guide the autonomous vehicle to make optimal maneuver decisions. To make our framework applicable to more scenarios, we further propose an improvedHEAD(HEAD++) framework that makes the autonomous vehicle adapt to various road structures, such as lane merging and diverging scenarios. Besides, we develop a style tuning module inHEAD++, which supports personalized driving style tuning. To mitigate high training costs, an efficient style tuning method with approximate gradient descent is proposed to reduce the number of training iterations. Extensive experiments demonstrate the effectiveness of our framework. Compared to state-of-the-art methods,HEAD++ reduces overall traffic disturbance by 23.3%-40.9%, lowers collision risk by 4.5%-17.8%, and improves passenger comfort by 13.1%-30.5%, while maintaining high traffic efficiency.

Similar subtrajectory search (SimSub) aims to find a subtrajectory (i.e., a segment) from a data trajectory (the trajectory to be queried) that closely resembles the query trajectory. Compared with similar trajectory search, SimSub can capture finer-grained similarity and is vital for various trajectory analysis tasks, such as trajectory clustering and join. However, SimSub may return a subtrajectory with extremely limited length, e.g., a single point, which may not align with the expectations of real-world applications. To solve this issue, we propose a constrained SimSub (cSimSub) problem, where the length of the returned subtrajectory must be greater than or equal to a user-specified integer$C$. We demonstrate that this problem can be solved exactly with a time complexity equivalent to$C$times the complexity of the trajectory distance measurement, given that the distance function can be computed using dynamic programming (DP). We also observe that when$C=1$, the solution of cSimSub differs from the vanilla trajectory distance computation (e.g., DTW) only in the state initialization of the DP matrix. Moreover, SimSub focuses on finding a subtrajectory with successive point indexes, which limits its applicability in certain scenarios, e.g., trajectory simplification. Thus, we extend it to sSimSub for trajectory simplification, aiming to find the most similar non-continuous subsequence of a trajectory to itself, with a length constraint of$C$. The subsequence, i.e., the simplified subtrajectory, obtained from sSimSub can achieve the best self-similarity. We conduct experiments on three public available datasets to demonstrate the effectiveness of the proposals. The results show that integrating sSimSub into typical query methods, e.g., KNN query, can achieve higher accuracy of these methods in simplified trajectory databases compared with other well-known trajectory simplification algorithms.

In many real-world applications (e.g., email networks, social networks, and phone call networks), the relationships between entities can be modeled as a temporal graph, in which each edge is associated with a timestamp representing the interaction time. As a fundamental task in temporal graph analysis, triangle counting has received much attention, and several triangle models have been developed, including δ-temporal triangle, sliding-window triangle, and (δ 1,3 , δ 1,2 , δ 2,3 )-temporal triangle. In particular, the δ-temporal triangle, requiring the gap of timestamps of any two edges within it to be bounded by a threshold δ, has been demonstrated effective in many real applications, such as cohesiveness analysis, transitivity, clustering coefficient, and graph classification. In this paper, we study fast algorithms for counting δ-temporal triangles in a given query time window. We first propose an online algorithm, which enumerates all edges in the graph and for each edge, calculates how many δ-temporal triangles end with the edge. We further develop an efficient index-based solution, which maps δ-temporal triangles into points of the 2-dimensional space and further compactly organizes these points using hierarchical structures. Besides, we study the problem of binary δ-temporal triangle counting by considering the existence of δ-temporal triangle among three vertices. Experiments on large temporal graphs show that our online algorithm is up to 70× faster than the state-of-the-art algorithm, and our index-based algorithm is up to 10 8 × faster than the online algorithm.

Searching for similar trajectories from a database is an important way for extracting human-understandable knowledge. However, due to the huge volume of trajectories and high computation complexity of distance between trajectories, it is difficult to search for exact results, which motivates the research of approximating approaches. In this study, we propose a learning to hash method for trajectory similarity computation and search, called Traj2Hash, which consists of a two-channel trajectory encoder and a hash layer to encode trajectories into Euclidean and Hamming space, respectively. The embeddings of trajectories obtained from the encoder are capable of preserving the reverse symmetric property and more representative due to the reverse augmentation and the lower-bound induced read-out layer. Moreover, we design a decomposed grid representation in the encoder to make the model lighter and better. In the model training phase, we combine a weighted mean squared error loss and a ranking-based hashing loss to enable the model similarity-aware and representations self-structured, respectively, in which a fast trajectory triplet generation method is leveraged to enrich the training corpus. Extensive experiments conducted on real data offer evidence of the effectiveness and efficiency of the proposed model.

The popularity of mobile devices has led to the increased attention of Spatial Crowdsourcing (SC), a framework that assigns location-sensitive tasks to mobile workers. Task recommendation is crucial in helping workers discover attractive tasks. Existing studies have focused on modeling workers' preferences from past task-performing patterns, but their performance is sub-optimal due to the strong coupling of sequentiality, spatiality, and temporality. Moreover, achieving the highest preference-based utility of workers in most of the existing task recommendation studies is inferior to the benefits of the SC platform and the satisfaction of workers in a long range, due to the lower task coverage rate and the poor diversity in a worker's recommended list. To address these problems, we propose a Diversity-Coverage Balanced Task Recommendation (DCBTaskRec) framework. Specifically, we first introduce a decoupled worker preference learning model that adopts self-attention networks as the backbone and decouples the modeling of multiple factors in attention scores. Additionally, we provide an optimal diveristy-aware approach to maximize the recommendation diversity while keeping high preference-based utility of workers to satisfy the multiple tastes of workers. From the side of the SC platform, we also provide two approaches (i.e., greedy coverage-aware approach and diversity-coverage balanced approach) to achieve high coverage and provide a trade-off between diversity and coverage, respectively. Extensive experiments offer insight into the effectiveness of the proposed framework.

Cost estimation is of great importance in query optimization. However, traditional optimizers compute the cost based on heuristics, sacrificing accuracy for efficiency. In recent years, learning-based cost estimation models have achieved high accuracy. However, their poor robustness and inefficiency lead to their failure to meet the needs of practical scenarios. We propose a lightweight and Database-Agnostic Cost Estimation model (DACE) to address the above limitations. To further improve the effectiveness of DACE, we design a tree-structure-based loss adjustment strategy to learn sub-plan information and solve the information redundancy problem. As a pretrained estimator, DACE can efficiently make accurate predictions on unseen databases. For more complex scenarios, we fine-tune DACE with LoRA. The excellent efficiency allows DACE to adapt to challenging scenarios with minimal effort. As a pretrained encoder, DACE can improve the accuracy and robustness of other cost estimation models through knowledge integration and solve the notorious cold start problem. Extensive experiments have shown that DACE's accuracy, efficiency, and robustness are much better than existing methods.

Autonomous driving is an emerging technology that has advanced rapidly over the last decade. Modern transportation is expected to benefit greatly from a wise decision-making framework of autonomous vehicles, including the improvement of mobility and the minimization of risks and travel time. However, existing methods either ignore the complexity of environments only fitting straight roads, or ignore the impact on surrounding vehicles during optimization phases, leading to weak environmental adaptability and incomplete optimization objectives. To address these limitations, we propose a pArameterized decision-making framework with mU lti-modality percepTiOn based on deep reinforcement learning, called AUTO. We conduct a comprehensive perception to capture the state features of various traffic participants around the autonomous vehicle, based on which we design a graph-based model to learn a state representation of the multi-modal semantic features. To distinguish between lane-following and lane-changing, we decompose an action of the autonomous vehicle into a parameterized action structure that first decides whether to change lanes and then computes an exact action to execute. A hybrid reward function takes into account aspects of safety, traffic efficiency, passenger comfort, and impact to guide the framework to generate optimal actions. In addition, we design a regularization term and a multi-worker paradigm to enhance the training. Extensive experiments offer evidence that AUTO can advance state-of-the-art in terms of both macroscopic and microscopic effectiveness.

Autonomous driving is a rapidly advancing field that promises to revolutionize the transportation industry through an intelligent perception-and-decision paradigm. Despite decades of research, existing methods are limited in adapting to complex scenarios or expanding to unseen situations, which pose significant challenges to the development of autonomous driving. Inspired by the process of human learning to drive, autonomous vehicles can prioritize developing driving capabilities in basic scenarios and then extending the atomic abilities to more complex scenarios. To this end, we proposed a perception-and-decision framework, called ATEND, which consists of an adaptive perception module and a maneuver decision module. Specifically, the perception module based on Variational Autoencoder is proposed to map perceptual data of complex scenarios into basic scenarios. Then the reinforcement learning-based decision module can make high-level decisions in transformed scenarios. Once ATEND learns to drive in basic scenarios, it can achieve safe and efficient driving in real scenarios without additional training. Extensive experiments in different traffic scenarios evidence that the proposed framework advances the state of the art in terms of both macroscopic and microscopic effectiveness.

Autonomous driving systems (ADSs) have the potential to revolutionize transportation by improving traffic safety and efficiency. As the core component of ADSs, maneuver decision aims to make tactical decisions to accomplish road following, obstacle avoidance, and efficient driving. In this work, we consider a typical but rarely studied task, called Target-Lane-Entering (TLE), where an autonomous vehicle should enter a target lane before reaching an intersection to ensure a smooth transition to another road. For navigation-assisted autonomous driving, a maneuver decision module chooses the optimal timing to enter the target lane in each road section, thus avoiding rerouting and reducing travel time. To achieve the TLE task, we propose a ruLe-aided reINforcement lEarning framework, called LINE, which combines the advantages of RL-based policy and rule-based strategy, allowing the autonomous vehicle to make target-oriented maneuver decisions. Specifically, an RL-based policy with a hybrid reward function is able to make safe, efficient, and comfortable decisions while considering the factors of target lanes. Then a strategy of rule revision aims to help the policy learn from intervention and block the risk of missing target lanes. Extensive experiments based on the SUMO simulator confirm the effectiveness of our framework. The results show that LINE achieves state-of-the-art driving performance with over 95% task success rate.

Autonomous driving is an emerging technology that has developed rapidly over the last decade. There have been numerous interdisciplinary challenges imposed on the current transportation system by autonomous vehicles. In this paper, we conduct an algorithmic study on the autonomous vehicle decision-making process, which is a fundamental problem in the vehicle automation field and the root cause of most traffic congestion. We propose a perception-and-decision framework, called HEAD, which consists of an enHanced pErception module and a mAneuver Decision module. HEAD aims to enable the autonomous vehicle to perform safe, efficient, and comfortable maneuvers with minimal impact on other vehicles. In the enhanced perception module, a graph-based state prediction model with a strategy of phantom vehicle construction is proposed to predict the one-step future states for multiple surrounding vehicles in parallel, which deals with sensor limitations such as limited detection range and poor detection accuracy under occlusions. Then in the maneuver decision module, a deep reinforcement learning-based model is designed to learn a policy for the autonomous vehicle to perform maneuvers in continuous action space w.r.t. a parameterized action Markov decision process. A hybrid reward function takes into account aspects of safety, efficiency, comfort, and impact to guide the autonomous vehicle to make optimal maneuver decisions. Extensive experiments offer evidence that HEAD can advance the state of the art in terms of both macroscopic and microscopic effectiveness.

In this paper, for the first time, we introduce the concepts of window-CCs and window-SCCs on undirected and directed temporal graphs, respectively. We then study the queries of window-CC and window-SCC by developing several efficient index-based query solutions. The space costs of the best indices are linear to the sizes of the temporal graphs. The extensive experimental evaluation on 12 real-world datasets demonstrates the high efficiency and effectiveness of the proposed solutions. In the future, we will develop distributed index construction algorithms, which would be useful for very large temporal graphs containing billions of edges. In the future, we will implement our algorithms by using a distributed computing platform (e.g., Pregel), which would be very useful when the temporal graph is too large to be kept by a single machine.

Velocity control in autonomous driving is an emerging technology that has achieved rapid progress over the last decade. However, existing velocity control models are developed in single-lane scenarios and ignore the negative impacts caused by harsh velocity changes. In this work, we propose a velocity control framework based on reinforcement learning, called RISE (contRol velocIty for autonomouS vEhicle). In multi-lane circumstances, RISE improves velocity decisions regarding the autonomous vehicle itself, while minimizing impacts on rear vehicles. To achieve multiple objectives, we propose a hybrid reward function to rate each velocity decision from four aspects: safety, efficiency, comfort, and negative impact to guide the autonomous vehicle. Among these reward factors, the negative impact is used to penalize the harsh actions of the autonomous vehicle, thus prompting it to reduce the negative impacts on its rear vehicles. To detect the latent perturbations among surrounding vehicles in multiple lanes, we propose an attention-based encoder to learn the positions and interactions from an impact graph. Extensive experiments evidence that RISE enables safe driving, and outperforms state-of-the-art methods in efficiency, comfort, and alleviating negative impacts.