The largest portion of urban congestion is caused by 'phantom' traffic jams, causing significant delay travel time, fuel waste, and air pollution. It frequently occurs in high-density traffics without any obvious signs of accidents or roadworks. The root cause of 'phantom' traffic jams in one-lane traffics is the sudden change in velocity of some vehicles (i.e. harsh driving behavior (HDB)), which may generate a chain reaction with accumulated impact throughout the vehicles along the lane. This paper makes the first attempt to address this notorious problem in a one-lane traffic environment through velocity control of autonomous vehicles. Specifically, we propose a velocity control framework, called PATROL (sPAtial-temporal ReinfOrcement Learning). First, we design a spatial-temporal graph inside the reinforcement learning model to process and extract the information (e.g. velocity and distance difference) of multiple vehicles ahead across several historical time steps in the interactive environment. Then, we propose an attention mechanism to characterize the vehicle interactions and an LSTM structure to understand the vehicles' driving patterns through time. At last, we modify the reward function used in previous velocity control works to enable the autonomous driving agent to predict the HDB of preceding vehicles and smoothly adjust its velocity, which could alleviate the chain reaction caused by HDB. We conduct extensive experiments to demonstrate the effectiveness and superiority of PATROL in alleviating the 'phantom' traffic jam in simulation environments. Further, on the real-world velocity control dataset, our method significantly outperforms the existing methods in terms of driving safety, comfortability, and efficiency.

PATROL: A Velocity Control Framework for Autonomous Vehicle via Spatial-Temporal Reinforcement Learning

Human mobility recovery is of great importance for a wide range of location-based services. However, recovering human mobility is not trivial because of three challenges: 1) complex transition patterns among locations; 2) multi-level periodicity and shifting periodicity of human mobility; 3) sparsity of the collected trajectory data. In this paper, we propose PeriodicMove, a neural attention model based on graph neural network for human mobility recovery from lengthy and sparse trajectories. In PeriodicMove, we first construct a directed graph for each trajectory and capture complex location transition patterns using graph neural network. Then, we design two attention mechanisms which capture multi-level periodicity and shifting periodicity of human mobility respectively. Finally, a spatial-aware loss function is proposed to incorporate spatial proximity into the model optimization, which alleviates the data sparsity problem. We perform extensive experiments and the evaluation results demonstrate that PeriodicMove yields significant improvements over the competitors on two representative real-life mobility datasets. In addition, by providing high-quality mobility data, our model can benefit a variety of mobility-oriented downstream applications.

PeriodicMove: Shift-aware Human Mobility Recovery with Graph Neural Network

Sarcasm is a linguistic phenomenon indicating a discrepancy between literal meanings and implied intentions. Due to its sophisticated nature, it is usually difficult to be detected from the text itself. As a result, multi-modal sarcasm detection has received more and more attention in both academia and industries. However, most existing techniques only modeled the atomic-level inconsistencies between the text input and its accompanying image, ignoring more complex compositions for both modalities. Moreover, they neglected the rich information contained in external knowledge, e.g., image captions. In this paper, we propose a novel hierarchical framework for sarcasm detection by exploring both the atomic-level congruity based on multi-head cross attentions and the composition-level congruity based on graph neural networks, where a post with low congruity can be identified as sarcasm. In addition, we exploit the effect of various knowledge resources for sarcasm detection. Evaluation results on a public multi-modal sarcasm detection dataset based on Twitter demonstrate the superiority of our proposed model.

Towards Multi-Modal Sarcasm Detection via Hierarchical Congruity Modeling with Knowledge Enhancement

The ubiquity of mobile devices and the accompanying deployment of sensing technologies have resulted in a massive amount of trajectory data. One important fundamental task is trajectory similarity computation, which is to determine how similar two trajectories are. To enable effective and efficient trajectory similarity computation, we propose a novel robust model, namely Contrastive Learning based Trajectory Similarity Computation (CL-TSim). Specifically, we employ a contrastive learning mechanism to learn the latent representations of trajectories and then calculate the dissimilarity between trajectories based on these representations. Compared with sequential auto-encoders that are the mainstream deep learning architectures for trajectory similarity computation, CL-TSim does not require a decoder and step-by-step reconstruction, thus improving the training efficiency significantly. Moreover, considering the non-uniform sampling rate and noisy points in trajectories, we adopt two type of augmentations, i.e., point dowm-sampling and point distorting, to enhance the robustness of the proposed model. Extensive experiments are conducted on two widely-used real-world datasets, i.e., Porto and ChengDu, which demonstrate the superior effectiveness and efficiency of the proposed model.

Efficient Trajectory Similarity Computation with Contrastive Learning

Trajectory prediction enables the fast and accurate response of autonomous driving navigation in complex and dense traffics. In this paper, we present a novel trajectory prediction network called Heterogeneous Graph Aggregation (HeGA) for high-density heterogeneous traffic, where the traffic agents of various categories interact densely with each other. To predict the trajectory of a target agent, HeGA first automatically selects neighbors that interact with it by our proposed adaptive neighbor selector, and then aggregates their interactions based on a novel two-phase aggregation transformer block. At last, the historical residual connection LSTM enhances the historical information awareness and decodes the spatial coordinates as the prediction results. Extensive experiments on real data demonstrate that the proposed network significantly outperforms the existing state-of-the-art competitors by over 27% on average displacement error (ADE) and over 31% on final displacement error (FDE). We also deploy HeGA in a state-of-the-art framework for autonomous driving, demonstrating its superior applicability based on three simulated environments with different densities and complexities.

HeGA: Heterogeneous Graph Aggregation Network for Trajectory Prediction in High-Density Traffic

Although many applications take subtrajectories as basic units for analysis, there is little research on the similar subtrajectory search problem aiming to return a portion of a trajectory (i.e., subtrajectory), which is the most similar to a query trajectory. We find that in some special cases, when a grid-based metric is used, this problem can be formulated as a reading comprehension problem, which has been studied extensively in the field of natural language processing (NLP). By this formulation, we can obtain faster models with better performance than existing methods. However, due to the difference between natural language and trajectory (e.g., spatial relationship), it is impossible to directly apply NLP models to this problem. Therefore, we propose a Similar Subtrajectory Search with a Graph Neural Networks framework. This framework contains four modules including a spatial-aware grid embedding module, a trajectory embedding module, a query-context trajectory fusion module, and a span prediction module. Specifically, in the spatial-aware grid embedding module, the spatial-based grid adjacency is constructed and delivered to the graph neural network to learn spatial-aware grid embedding. The trajectory embedding module aims to model the sequential information of trajectories. The purpose of the query-context trajectory fusion module is to fuse the information of the query trajectory to each grid of the context trajectories. Finally, the span prediction module aims to predict the start and the end of a subtrajectory for the context trajectory, which is the most similar to the query trajectory. We conduct comprehensive experiments on two real world datasets, where the proposed framework outperforms the state-of-the-art baselines consistently and significantly.

Efficient and Effective Similar Subtrajectory Search: A Spatial-aware Comprehension Approach

Prerequisite relations among concepts are important for a wide range of educational applications, such as intelligent tutoring and curriculum planning. However, concept prerequisite relation learning is not trivial due to the sparsity of prerequisite relations. In this paper, we propose a contextual-knowledge-aware concept prerequisite relation learning approach called ConLearn. Four unique properties of the proposed approach are: (1) It transfers knowledge from large language model BERT to improve contextual representations of concepts; (2) It captures concept prerequisite transition patterns by applying graph neural network on concept prerequisite graph; (3) It is equipped with self-attention mechanism to fuse information from related concepts for target concept prerequisite relation classification; (4) No handcrafted features are used in our model, which makes our model easy to implement in downstream applications. Extensive experiments on three representative datasets demonstrate that our approach significantly outperforms the state-of-the-art methods. 

ConLearn: Contextual-knowledge-aware Concept Prerequisite Relation Learning with Graph Neural Network

Open-domain question answering is a crucial task that often requires accessing external information. Existing methods typically adopt a single-turn retrieve-then-read approach, where relevant documents are first retrieved, and questions are then answered based on the retrieved information. However, there are cases where answering a question requires implicit knowledge that is not directly retrievable from the question itself. In this work, we propose a novel question-answering pipeline called BeamSearchQA. Our approach leverages large language models to iteratively generate new questions about the original question, enabling an iterative reasoning process. By iteratively refining and expanding the scope of the question, our method aims to capture and utilize hidden knowledge that may not be directly obtainable through retrieval. We evaluate our approach on the widely-used open-domain NQ and WebQ datasets. The experimental results demonstrate that BeamSearchQA significantly outperforms other zero-shot baselines, indicating its effectiveness in tackling the challenges of open-domain question answering.

BeamSearchQA: Large Language Models are Strong Zero-Shot QA Solver

Trajectory-User Linking (TUL) aiming to identify users of anonymous trajectories, has recently received increasing attention due to its wide range of applications, such as criminal investigation and personalized recommendation systems. In this paper, we propose a flexible Semi-Supervised framework for Trajectory-User Linking, namely S2TUL, which includes five components: trajectory-level graph construction, trajectory relation modeling, location-level sequential modeling, a classification layer and greedy trajectory-user relinking. The first two components are proposed to model the relationships among trajectories, in which three homogeneous graphs and two heterogeneous graphs are firstly constructed and then delivered into the graph convolutional networks for converting the discrete identities to hidden representations. Since the graph constructions are irrelevant to the corresponding users, the unlabelled trajectories can also be included in the graphs, which enables the framework to be trained in a semi-supervised way. Afterwards, the location-level sequential modeling component is designed to capture fine-grained intra-trajectory information by passing the trajectories into the sequential neural networks. Finally, these two level representations are concatenated into a classification layer to predict the user of the input trajectory. In the testing phase, a greedy trajectory-user relinking method is proposed to assure the linking results satisfy the timespan overlap constraint. We conduct extensive experiments on three public datasets with six representative competitors. The evaluation results demonstrate the effectiveness of the proposed framework.

https://dl.acm.org/doi/pdf/10.1145/3539597.3570410

Hao Sun

Papers

Efficient and Effective Similar Subtrajectory Search: A Spatial-aware Comprehension Approach

ConLearn: Contextual-knowledge-aware Concept Prerequisite Relation Learning with Graph Neural Network

Efficient Trajectory Similarity Computation with Contrastive Learning

BeamSearchQA: Large Language Models are Strong Zero-Shot QA Solver

S2TUL: A Semi-Supervised Framework for Trajectory-User Linking