Accurate traffic prediction is crucial for urban traffic management. Spatial-temporal graph neural networks, which combine graph neural networks with time series processing, have been extensively employed in traffic prediction. However, traditional graph neural networks only capture pairwise spatial relationships between road network nodes, neglecting high-order interactions among multiple nodes. Meanwhile, most work for extracting temporal dependencies suffers from implicit modeling and overlooks the internal and external dependencies of time series. To address these challenges, we propose a Geometric Algebraic Multi-order Graph Neural Network (GA-MGNN). Specifically, in the temporal dimension, we design a convolution kernel based on the rotation matrix of geometric algebra, which not only learns internal dependencies between different time steps in time series but also external dependencies between time series and convolution kernels. In the spatial dimension, we construct a tokenized hypergraph and integrate dynamic graph convolution with attention hypergraph convolution to comprehensively capture multi-order spatial dependencies. Additionally, we design a segmented loss function based on traffic periodic information to further improve prediction accuracy. Extensive experiments on seven real-world datasets demonstrate that GA-MGNN outperforms state-of-the-art baselines.

Treating each intersection as basic agent, multi-agent reinforcement learning (MARL) methods have emerged as the predominant approach for distributed adaptive traffic signal control (ATSC) in multi-intersection scenarios, such as arterial coordination. MARL-based ATSC currently faces two challenges: disturbances from the control policies of other intersections may impair the learning and control stability of the agents; and the heterogeneous features across intersections may complicate coordination efforts. To address these challenges, this study proposes a novel MARL method for distributed ATSC in arterials, termed the Distributed Controller for Heterogeneous Intersections (DCHI). The DCHI method introduces a Neighborhood Experience Sharing (NES) framework, wherein each agent utilizes both local data and shared experiences from adjacent intersections to improve its control policy. Within this framework, the neural networks of each agent are partitioned into two parts following the Knowledge Homogenizing Encapsulation (KHE) mechanism. The first part manages heterogeneous intersection features and transforms the control experiences, while the second part optimizes homogeneous control logic. Experimental results demonstrate that the proposed DCHI achieves efficiency improvements in average travel time of over 30% compared to traditional methods and yields similar performance to the centralized sharing method. Furthermore, vehicle trajectories reveal that DCHI can adaptively establish green wave bands in a distributed manner. Given its superior control performance, accommodation of heterogeneous intersections, and low reliance on information networks, DCHI could significantly advance the application of MARL-based ATSC methods in practice.

Crafting a dynamic and accurate resilience assessment method for urban transportation, marked by complex road networks and frequent disturbances, poses a significant challenge. Existing work mainly focuses on statically assessing historical traffic resilience and cannot dynamically divide spatial regions according to disturbance scales. In this article, we propose a predictive and multigranularity assessment method. First, we develop an attention-based spatial-temporal hypergraph neural controlled differential equation model, which can accurately predict traffic conditions under disturbances. Second, we construct a multigranularity disturbance propagation model that adaptively divides a traffic network into multiple granularities according to disturbance scales. Then, we design a real-time resilience assessment algorithm capable of quantifying spatial-temporal dynamic resilience indicators for each granularity area. Extensive experiments on urban transportation in California during heavy rainfall reveal an inverse relationship between California's resilience and rainfall intensity. In addition, its downtown exhibits strong resilience, while coastal and interior areas show relatively weaker resilience, with some interior areas experiencing prolonged recovery times.

The ability of road networks to withstand external disturbances is a crucial measure of transportation system performance, where resilience distinctly emerges as an effective perspective for its unique insights into the system’s resistance and recovery capabilities. In the face of unforeseen resilience disturbance events, predictive and accurate assessment of road network resilience is essential for better traffic regulation and emergency response management. However, existing resilience assessment methods of road networks are insufficient: they lack reliable real-time big-data analysis, do not possess predictive capabilities for guiding decision-making, and have a narrow view with single-dimensional resilience indicators. To address these issues, focusing on rainfall disturbance scenarios, this work introduces a novel resilience assessment method, which is predictive and real-time, consisting of two components: a deep learning traffic indicator prediction model and a comprehensive resilience assessment model. Firstly, we propose a two-stage traffic indicator prediction model, namely the Conditional Diffusion-Reconstruction-based Graph Neural Network (CDRGNN), which particularly enhances disturbance-scenario prediction accuracy, thereby providing reliable foresight in aid of the following assessments. Subsequently, we develop a resilience assessment model featuring structural-functional comprehensive resilience indicators established through shortest-path aggregation, and the overall resilience assessment is performed through comparative analysis using indicators obtained in real-time with historical non-disruptive resilience benchmarks. In a case study focusing on heavy rainfall disturbances on a road network in California, the United States, abundant experiments and visualizations are conducted to demonstrate the rationality of our proposed comprehensive resilience indicators as well as the precision and reliability of these predictive resilience assessment outcomes. • A predictive and real-time resilience assessment method helps emergency response. • Our diffusion-model-based reconstruction helps separate potential anomalous features. • Our dynamic graph and fusion output methods improve traffic indicator prediction. • Shortest-path aggregated resilience indicators have better disturbance sensibility. • A case study shows the reliability and accuracy of our predictive assessment method.

Due to the influence of global climate anomalies, abnormal weather conditions such as heavy rainfall have become more frequent in recent years, posing a significant threat to the operation of transportation systems. An effective assessment of the resilience of the transportation system before and during heavy rain can alert the transportation department to take necessary emergency actions. However, existing methods for assessing the rainfall resilience of transportation networks mostly suffer from the following problems: (1) Simulation methods for modeling rainfall impacts lack realism; (2) After-the-fact evaluations of resilience cannot offer advance warning prior to or during a heavy rain event. To address above problems, we present a novel resilience assessment methodology for evaluating the resilience of road networks in real-time during heavy rainfall scenarios. In this methodology, we propose the temporal decomposition-based dynamic multi-granularity graph neural network (TD2MG2NN) for long-term traffic speed forecasting, providing a perspective on the future evolution of traffic states for accurate resilience assessment. In addition, we construct a composite traffic resilience indicator, designed to comprehensively reflect changes in the spatial–temporal resilience of the transportation system during heavy rain. Experimental results on four publicly real datasets indicate that the prediction performance of TD2MG2NN outperforms state-of-the-art models. The assessment results for the transportation road network in California demonstrate that the comprehensive resilience indicator is superior to single functional resilience indicator and the real-time methodology for evaluating resilience can accurately depict and predict the operation of the road network system under heavy rainfall scenarios.

Ensuring the reliability of deep neural networks (DNNs) is paramount in safety-critical applications. Although introducing supplementary fault-tolerant mechanisms can augment the reliability of DNNs, an efficiency tradeoff may be introduced. This study reveals the inherent fault tolerance of neural networks, where individual neurons exhibit varying degrees of fault tolerance, by thoroughly exploring the structural attributes of DNNs. We thereby develop a hardware/software collaborative method that guarantees the reliability of DNNs while minimizing performance degradation. We introduce the neuron vulnerability factor (NVF) to quantify the susceptibility to soft errors. We propose two efficient methods that leverage the NVF to minimize the negative effects of soft errors on neurons. First, we present a novel computational scheduling scheme. By prioritizing error-prone neurons, the expedited completion of their computations is facilitated to mitigate the risk of neural computing errors that arise from soft errors without sacrificing efficiency. Second, we propose the NVF-guided heterogeneous memory system. We employ variable-strength error-correcting codes and tailor their error-correction mechanisms to the vulnerability profile of specific neurons to ensure a highly targeted approach for error mitigation. Our experimental results demonstrate that the proposed scheme enhances the neural network accuracy by 18% on average, while significantly reducing the fault-tolerance overhead.

It is quite challenging to establish a prompt and reliable prognosis assessment for acquired brain injury (ABI) patients with persistent severe disorders of consciousness (DOC) like unconscious comatose and unresponsive wakefulness syndrome (a.k.a., vegetative state). Recent advances in brain functional imaging and functional net-work analysis have demonstrated its potential in determining the consciousness level and prognostic outcome for ABI patients with DOC. However, the diagnostic and prognostic usefulness of the whole-brain functional connectome based on advanced machine learning techniques has not been fully evaluated. The first aim of this study is to predict the outcome of individual unconscious ABI patients during a three-month follow-up. The second aim is to conduct precise individualized differentiation among different consciousness levels for exploring the neurobiological mechanisms underlying DOC. Based on resting-state fMRI, we construct large-scale functional networks by using a weighted sparse model, which ensures sparsity and interpretability by preserving strong functional connections. The functional connection strengths are exploited as features for outcome prediction and consciousness level differentiation. We achieve significantly improved consciousness level classification (accuracy: 84.78%) and recovery outcome prediction (accuracy: 89.74%) compared to other network construction methods. More importantly, we reveal the contributive connections across the entire brain in both tasks. These connections could serve as the potential biomarkers for better understanding of consciousness and further provide new insight into the development of diagnostic, prognostic, and effective therapeutic guidelines for ABI patients with DOC.

Locating multiple sources in an unknown environment based on their signal strength is called a multi-source location problem. In recent years, there has been great interest in deploying autonomous devices to solve it. A particle swarm optimizer (PSO) is a widely employed source location method. Yet most work in this field focuses on a flat search space while ignoring height information. An unmanned aerial vehicle (UAV) has a coarser but wider view as it flies higher. Inspired by such facts, this paper focuses on improving the efficiency of locating sources by utilizing height information through UAVs. A novel source location model is designed where their sensing range gradually increases as their flying height rises, but their obtained signal strength fades away. It can be directly deployed to existing PSO-based multi-source location methods and improve their performance, especially in a large-scale environment with sparse sources. UAVs can spontaneously switch their search schemes between a rough search at a higher height and a fine one at a lower height. Experimental results of three PSO-based methods show their significant improvement after deploying our model. Given the same computation resources, its deployment leads to over 30% hike in both location accuracy and speed. This represents a great advance to the field of source location.

Deep learning models for short-term travel speed prediction on urban expressways, such as the convolutional neural network (CNN), still present several limitations in multiscale spatiotemporal feature extraction. Hence, in this paper, three hybrid CNN models are proposed to improve the basic CNN model with regard to three target aspects for short-term (i.e., 5 min) travel speed prediction on urban expressways. More specifically, long short-term memory (LSTM), AutoEncoder (AE), and Inception module are incorporated into the basic CNN model to capture multiscale spatiotemporal features of travel speed data effectively and improve the accuracy and robustness of the basic CNN model. Based on loop detector data collected on the Yan’an expressway in Shanghai, the proposed hybrid CNN models are trained and tuned. To validate the improvements on the target aspects, a comprehensive comparison is conducted using a classical statistical model (i.e., autoregressive integrated moving average), a typical shallow neural network model (i.e., artificial neural network), and two basic deep learning models (i.e., recurrent neural network and CNN). Results show that the prediction accuracies of all the proposed hybrid CNN models exceed 96% and the mean absolute errors are less than 2.5 km/h, which are superior to other models. In terms of target improving aspects, two new metrics were introduced, and the proposed models, especially the AE–CNN model, showed better robustness under various input data structures and traffic states. The LSTM–CNN model outperformed the other models in learning time-series features, and the Inception–CNN model is superior in reproducing the dynamics of traffic congestion patterns on urban expressways.

Speed plays a significant role in evaluating the evolution of traffic status, and predicting speed is one of the fundamental tasks for the intelligent transportation system. There exists a large number of works on speed forecast; however, the problem of long-term prediction for the next day is still not well addressed. In this paper, we propose a multiscale spatio-temporal feature learning network (MSTFLN) as the model to handle the challenging task of long-term traffic speed prediction for elevated highways. Raw traffic speed data collected from loop detectors every 5 min are transformed into spatial-temporal matrices; each matrix represents the one-day speed information, rows of the matrix indicate the numbers of loop detectors, and time intervals are denoted by columns. To predict the traffic speed of a certain day, nine speed matrices of three historical days with three different time scales are served as the input of MSTFLN. The proposed MSTFLN model consists of convolutional long short-term memories and convolutional neural networks. Experiments are evaluated using the data of three main elevated highways in Shanghai, China. The presented results demonstrate that our approach outperforms the state-of-the-art work and it can effectively predict the long-term speed information.

Particle Swarm Optimization (PSO) deteriorates when facing a high-noise environment. To address this issue, one popular mechanism is the resampling method that is based on re-evaluations to find the true fitness value. However, the budget for re-evaluations in PSO is limited. In this paper, we intend to integrate a Stochastic Point Location (SPL) method into PSO to alleviate the impacts of noise on the evaluation of true fitness. SPL deals with the problem of a learning mechanism locating a target point on the line in noisy environment. Up to now, Adaptive Step Searching is the fastest algorithm in solving the SPL problem and shows great anti-noise performance. This paper investigates two effective hybrid PSO approaches, by integrating PSO and PSO-Equal Resampling with Adaptive Step Searching. The simulation results and comparisons on 20 large-scale benchmark optimization functions in noisy environments demonstrate the superiority of the proposed approaches in terms of optimization accuracy and convergence rate.

In this paper, we present a new approach to detect traffic signs based on cascaded convolutional neural networks (CNNs). First, the local binary pattern (LBP) feature detector and the AdaBoost classifier are combined to extract regions of interest (ROI) for coarse selection. Next, cascaded CNNs are employed to reduce negative samples of ROI for traffic sign recognition. Compared with the conventional CNN, our CNN contains three convolutional layers and its classification part is replaced by the support vector machine (SVM). The German traffic sign detection benchmark is used and experimental results demonstrate that the proposed method can achieve competitive results when compared with the state-of-the-art approaches.

In this paper, a fast CU partition algorithm for H.264 to HEVC transcoding based on Fisher Discriminant Analysis is proposed. Using the classification model built with the extracted features from H.264 bitstream, the CU splitting of depth 0 and 1 can be directly determined without rate distortion optimization process, and a simple mode mapping method is used to determine CU splitting in depth 2. To ensure the accuracy of classification model, an online learning strategy is designed to update the model thresholds and weight vectors in time. The experimental results show that the proposed algorithm obtains a speed-up to 1.90× on average with 2.75% BD-rate loss under the low-delay P configuration.

A learning automaton (LA) is a powerful tool for reinforcement learning. Its action probability vector plays two roles: 1) deciding when it converges, i.e., total computing budget it has used, and 2) allocating computing budget among actions to identify the optimal one. These two intertwined roles lead to a problem: the computing budget mostly goes to the currently estimated optimal action due to its high action probability regardless whether such budget allocation can help identify the true optimal one or not. This work proposes a new class of LA that avoids the use of its action probability vector for computing budget allocation. Instead we use such vector only to determine if it converges and then employ optimal computing budget allocation to accomplish the allocation of computing budget in a way that maximizes the probability of identifying the true optimal actions. ε-optimality is proven. Simulations verify its advantages over existing algorithms. A learning automaton (LA) represents an important leaning mechanism with applications in automated system design, biological systems, computer vision, and transportation. It updates its action probability vector in accordance with the inputs received from the environment to improve its performance. It acts as an adaptive controller in modeling a process as well as generating appropriate control signals. The existing LAs simply employ heuristics to update their action probability vectors and then use the vectors for ordinal optimization and determining the computing budget size. This work separates ordinal optimization from the action probability vector and introduces optimal computing budget allocation to maximize the probability of selecting the true optimal action. Compared with the state-of-the-art methods in five popular environments, the proposed LA speeds up the learning efficiency ranging from 10.93% to 65.94%.

The best design among many can be selected through their accurate performance evaluation. When such evaluation is based on discrete event simulations, the design selection is extremely time-consuming. Ordinal optimization greatly speeds up this process. Optimal Computing-Budget Allocation (OCBA) has further accelerated it. Other kinds of OCBA have been introduced for reaching different goals, for example, to select the optimal subset of designs. However, facing the issue of subset ranking, which is a generalized form from problems selecting the best design or optimal subset, all the existing ones are insufficient. This work develops a new OCBA-based approach to address this subset ranking issue. Through mathematical deduction, its theoretical foundation is laid. Our numerical simulation results reveal that it indeed outperforms all the other existing methods in terms of probability of correct subset ranking and computational efficiency.

There have been many attempts to design brain-computer interfaces (BCIs) for wheelchair control based on steady state visual evoked potential (SSVEP), event-related desynchronization/synchronization (ERD/ERS) during motor imagery (MI) tasks, P300 evoked potential, and some hybrid signals. However, those BCI systems cannot implement the wheelchair navigation flexibly and effectively. In this paper, we propose a hybrid BCI scheme based on two-class MI and four-class SSVEP tasks. It cannot only provide multi-degree control for its user, but also allow the user implement the different types of commands in parallel. In order for the subject to learn the hybrid mental strategies effectively, we design a visual and auditory cues and feedback-based training paradigm. Furthermore, an algorithm based on entropy of classification probabilities is proposed to detect intentional control (IC) state for hybrid tasks, and ensure that multi-degree control commands are accurately and quickly generated. The experiment results attest to the efficiency and flexibility of the hybrid BCI for wheelchair control in the real-world.

Most of the traditional just-noticeable-distortion (JND) models in pixel domain compute the JND threshold by incorporating the spatial luminance adaptation effect and the textures contrast masking effect. Recently, with the rapid development of the computable models of visual attention, researchers started to improve the JND model by considering visual saliency of images, a foveated spatial JND model (FSJND) was proposed by incorporating the traditional visual characteristics and fovea characteristic of human eyes to enhance JND thresholds. However, the thresholds computed by the FSJND model may be overestimated for some high resolution images. In this paper, we proposed a new JND profile in pixel domain, in which a multi-level modulation function is built to reflect the effect of hierarchically selective visual attention on JND thresholds. The contrast masking is also considered in our modulation function to obtain more accurate JND thresholds. Compared with the lasted JND profiles, the proposed model can tolerate more distortion and has much better perceptual quality. The proposed JND model can be easily applied in many areas, such as compression, error protection, and so on.

Illumination change usually results in challenging problems for many computer vision applications such as recognition, tracking and motion analysis. In this paper, an illumination invariant object tracking approach is proposed. Video feature information is captured using a monogenic scale space representation. From this representation, multiscale phase information, which has the advantage of being invariant to illumination change, can be extracted. By minimizing an energy function, multiscale phase information between frames are matched. Based on the matched phase information, moving objects under high illumination variation can be successfully tracked. Experimental results show that the presented approach is robust to illumination change. As a comparison, we also demonstrate the tracking results from the mean shift tracker, it is proved that our approach outperforms the mean shift tracker under the high lighting change environment.

Rigidity and reflectivity are important properties of objects, identifying these properties is a fundamental problem for many computer vision applications like motion and tracking. In this paper, we extend our previous work to propose a motion analysis based approach for detecting the object's rigidity and reflectivity. This approach consists of two steps. The first step aims to identify object rigidity based on motion estimation and optic flow matching. The second step is to classify specular rigid and diffuse rigid objects using structure from motion and Procrustes analysis. We show how rigid bodies can be detected without knowing any prior motion information by using a mutual information based matching method. In addition, we use a statistic way to set thresholds for rigidity classification. Presented results demonstrate that our approach can efficiently classify the rigidity and reflectivity of an object.