Bus passenger flow prediction is a crucial task in bus transportation management and optimization, characterized by strong spatiotemporal dependencies and influenced by external factors such as weather and holidays. However, existing methods face challenges in responding to sudden events, modeling dynamic changes in bus network topology, and improving model generalization. To address these issues, this paper proposes a graph transfer learning-based approach for bus passenger flow prediction. First, a dynamic graph neural network is used to construct the passenger flow network, and Framelet Transform along with Self-Expressiveness Regularization is applied for denoising, enhancing data quality. Second, an adaptive neighborhood-aware dynamic graph convolutional network is introduced, integrating random mask enhancement, hop count perception fusion, and multi-channel spatiotemporal feature extraction for accurate passenger flow modeling. Furthermore, a combination of source data and source free transfer learning is leveraged to optimize feature distribution alignment through pseudo-label generation, graph diffusion, and consistency loss. Finally, a GRU is used for passenger flow prediction, and extensive experiments on real-world datasets test the proposed method. Results demonstrate superior accuracy and generalization performance compared to existing state of the art (SOTA) models.

Short-term passenger flow prediction is critical for intelligent scheduling and efficient operation of public transportation systems. However, existing methods often struggle to maintain robustness and generalizability when facing complex and heterogeneous noise sources, such as passenger flow noise including sensor-induced missing values and abrupt ridership fluctuations caused by unexpected events, and graph structural noise resulting from dynamic changes in the network topology due to route modifications or stop closures. To address these challenges, this paper proposes a novel deep learning framework: Robust Dynamic Graph Neural Networks (RDGNN), that integrates noise cleaning, dynamic graph modeling, and more efficient prediction. The proposed noise cleaning employs K-Nearest Neighbor imputation and Gaussian Mixture Models with a penalty function to mitigate data-level noise, while a graph structure denoising module is introduced to correct topological anomalies in the transit network and enhance the reliability of spatial representation. For feature modeling, the framework constructs a dynamic graph neural network from a subgraph perspective to capture both spatial dependencies and temporal dynamics, particularly suited to modeling interactions among transfer stations. A Liquid Neural Network is adopted as the prediction module, leveraging its strong adaptability and memory capacity to handle irregular and non-stationary time series, while remaining computationally efficient. The RDGNN model was trained and validated on real-world bus passenger flow data from Ames, Iowa, and further evaluated on the large-scale EXO dataset. Compared to state-of-the-art models, RDGNN consistently achieves better prediction accuracy, higher robustness to noise and missing data, and greater computational efficiency. Its strong and stable performance across both datasets, including in scenarios such as route disruptions and seasonal variation, demonstrates excellent generalization capability in diverse urban transit systems. The code is available at:https://github.com/XinyiZhou0318/A-Noise-Robust-Approach-Using-Dynamic-Graph-Neural-Networks-for-Bus-Passenger-Flow-Prediction

While much work has been done recently in the realm of model-based control of soft robots and soft-rigid hybrids, most works examine robots that have an inherently serial structure. While these systems have been prevalent in the literature, there is an increasing trend toward designing soft-rigid hybrids with intrinsically coupled elasticity between various degrees of freedom. In this work, we seek to address the issues of modeling and controlling such structures, particularly when underactuated. We introduce several simple models for elastic coupling, typical of those seen in these systems. We then propose a controller that compensates for the elasticity, and we prove its stability with Lyapunov methods without relying on the elastic dominance assumption. This controller is applicable to the general class of underactuated soft robots. After evaluating the controller in simulated cases, we then develop a simple hardware platform to evaluate both the models and the controller. Finally, using the hardware, we demonstrate a novel use case for underactuated, elastically coupled systems in "sensorless" force control.

While semantic segmentation has been extensively studied in the realm of regular perspective images, its application to fisheye images remains relatively unexplored. Existing literature on fisheye semantic segmentation mostly revolves around multi-task or multi-modal models, which are computationally intensive. This motivated us to assess the performance of current segmentation methods, specifically on fisheye images. Surprisingly, we discover that these methods do not yield satisfactory results when directly trained on fisheye datasets using a fully supervised approach. This can be attributed to the fact that the models are not designed to handle fisheye images, and the available fisheye datasets are not sufficiently large to effectively train complex models. To overcome these challenges, we propose a novel training method by employing Transfer Learning (TL) on existing semantic segmentation models that concentrate on a single task and modality. To achieve this, we investigate six different fine-tuning configurations using the WoodScape fisheye image segmentation dataset. Furthermore, we introduce a pre-training stage that learns from perspective images by applying a fisheye transformation before employing transfer learning. As a result, our proposed training pipeline demonstrates a remarkable 18.29% improvement in mean Intersection over Union (mIoU) compared to directly adopting the best existing segmentation methods for fish eye images.

The hydrometric prediction of water quantity is useful for a variety of applications, including water management, flood forecasting, and flood control. However, the task is difficult due to the dynamic nature and limited data of water systems. Highly interconnected water systems can significantly affect hydrometric forecasting. Consequently, it is crucial to develop models that represent the relationships between other system components. In recent years, numerous hydrological applications have been studied, including streamflow prediction, flood forecasting, and water quality prediction. Existing methods are unable to model the influence of adjacent regions between pairs of variables. In this paper, we propose a spatiotemporal forecasting model that augments the hidden state in Graph Convolution Recurrent Neural Network (GCRN) encoder-decoder using an efficient version of the attention mechanism. The attention layer allows the decoder to access different parts of the input sequence selectively. Since water systems are interconnected and the connectivity information between the stations is implicit, the proposed model leverages a graph learning module to extract a sparse graph adjacency matrix adaptively based on the data. Spatiotemporal forecasting relies on historical data. In some regions, however, historical data may be limited or incomplete, making it difficult to accurately predict future water conditions. Further, we present a new benchmark dataset of water flow from a network of Canadian stations on rivers, streams, and lakes. Experimental results demonstrate that our proposed model TransGlow significantly outperforms baseline methods by a wide margin.

We explore the idea of robotic mechanisms that can shift between soft and rigid states, with the long-term goal of creating robots that marry the flexibility and robustness of soft robots with the strength and precision of rigid robots. We present a simple yet effective method to achieve large and rapid stiffness variations by compressing and relaxing a flexure using cables. Next, we provide a differentiable modeling framework that can be used for motion planning, which simultaneously reasons about the modulated stiffness joints, tendons, rigid joints, and basic hydrodynamics. We apply this stiffness tuning and simulation recipe to create SoRiTu, an untethered soft-rigid robotic sea turtle capable of various swimming maneuvers.

Graph neural networks (GNNs) have been extensively used in a wide variety of domains in recent years. Owing to their power in analyzing graph-structured data, they have become broadly popular in intelligent transportation systems (ITS) applications as well. Despite their widespread applications in different transportation domains, there is no comprehensive review of recent advancements and future research directions that covers all transportation areas. Accordingly, in this survey, for the first time, we provide an overview of GNN studies in the general domain of ITS. Unlike previous surveys, which have been limited to traffic forecasting problems, we explore how GNN frameworks have evolved for different ITS applications, including traffic forecasting, demand prediction, autonomous vehicles, intersection management, parking management, urban planning, and transportation safety. Also, we micro-categorize the studies based on their transportation application to identify domain-specific research directions, opportunities, and challenges, which have been missing in previous surveys. Moreover, we identify unique and undiscussed research opportunities and directions, which is the result of reviewing a wide range of transportation applications. The neglected role of edge and graph learning in ITS applications, developing multi-modal models, and exploiting the power of unsupervised and reinforcement learning methods for developing more powerful GNNs are some examples of such new discussions in this survey. Finally, we have identified popular baseline models and datasets in each transportation domain, which facilitate the development and evaluation of future GNN-based frameworks.

Abstract In this article, first, we propose a novel unsupervised learning method based on a hierarchical Dirichlet process mixture of shifted‐scaled Dirichlet (SSD) distributions. Second, we extend it to a hierarchical Pitman–Yor process mixture of SSD distributions. The goal is to find a model that properly fits complex real‐world data. Our models are based on SSD distributions that are more flexible than Dirichlet distribution in fitting proportional data. Simultaneous data fitting (parameter estimate) and model selection (model complexity determination) are possible with the suggested methods. We applied batch and online variational inference for learning the models. The online setting allows us to feed our models with large‐scale streaming data. The effectiveness of our proposed models is evaluated by four realistic and challenging applications, namely, spam email detection, texture clustering, traffic sign detection, and vehicle detection. Experimental results demonstrate the potential of our models to fit proportional data.

Agent-based microsimulation has become the standard to analyze intelligent transportation systems, using disaggregate travel demand data for entire populations, data that are not typically readily available. Population synthesis approaches are thus needed. We present Composite Travel Generative Adversarial Network (CTGAN), a novel deep generative model to estimate the underlying joint distribution of a population, that is capable of reconstructing composite synthetic agents having tabular (e.g. age and sex) as well as sequential mobility data (e.g. trip trajectory and sequence). The CTGAN model is compared with other recently proposed methods such as the Variational Autoencoders (VAE) method, which has shown success in high dimensional tabular population synthesis. We evaluate the performance of the synthesized outputs based on distribution similarity, multi-variate correlations and spatio-temporal metrics. The results show the consistent and accurate generation of synthetic populations and their tabular and spatially sequential attributes, generated over varying spatial scales and dimensions.

Simulating the swimming of soft underwater robot remains challenging due to the absence of an efficient numerical framework that can effectively capture the geometrically nonlinear deformation of soft materials and structures when interacting with a liquid environment. Here, we address this by introducing a discrete differential geometry-based model that incorporates an implicit treatment of the elasticity of soft limbs and a fluid model with three different components: hydrodynamic drag, jetting, and virtual added mass. The physical engine can run faster than real-time on a single thread desktop processor. We experimentally validate this numerical simulation tool by performing tests using an untethered omni-directional star-shaped swimming soft robot that is capable of moving with multiple swimming gaits. Quantitative agreement between experiment and simulation indicates the potential application of such a numerical framework for robot design and for model-based control schemes.

Soft robots are capable of inherently safer interactions with their environment than rigid robots since they can mechanically deform in response to unanticipated stimuli. However, their complex mechanics can make planning and control difficult, particularly with tasks such as locomotion. In this work, we present a mobile and untethered underwater crawling soft robot, PATRICK, paired with a testbed that demonstrates closed-loop locomotion planning. PATRICK is inspired by the brittle star, with five flexible legs actuated by a total of 20 shape-memory alloy (SMA) wires, providing a rich variety of possible motions via its large input space. We propose a motion planning infrastructure based on a simple set of PATRICK's motion primitives, and provide experiments showing that the planner can command the robot to locomote to a goal state. These experiments contribute the first examples of closed-loop, state-space goal seeking of an underwater, untethered, soft crawling robot, and make progress towards full autonomy of soft mobile robotic systems.

We develop ensemble convolutional neural networks (CNNs) to classify the transportation mode of trip data collected as part of a large-scale smartphone travel survey in Montreal, Canada. Our proposed ensemble library is composed of a series of CNN models with different hyper-parameter values and CNN architectures. In our final model, we combine the output of CNN models using “average voting,” “majority voting,” and “optimal weights” methods. Furthermore, we exploit the ensemble library by deploying a random forest model as a meta-learner. The ensemble method with random forest as meta-learner shows an accuracy of 91.8% which surpasses the other three ensemble combination methods, and other comparable models reported in the literature. The “majority voting” and “optimal weights” combination methods result in prediction accuracy rates around 89%, while “average voting” is able to achieve an accuracy of only 85%.