With the continuous growth of urban traffic deat mand, traffic flow prediction faces modeling challenges due to its highly nonlinear spatiotemporal coupling characteristics and the complexities of multiple periods. Existing methods are typically applied directly to the original traffic flow sequences, relying on implicit temporal accumulation or static spatial topology to capture long-term dependencies. This makes it difficult to explicitly characterize the simultaneous trend changes, periodic disturbances, and cross-node spatial diffusion patterns within the traffic flow, leading to performance degradation, especially in long-term prediction scenarios. To address this, this paper proposes a Multi-Scale Spatiotemporal Traffic Flow Prediction Model (MSTF), which explicitly and structurally exposes the multi-scale spatiotemporal characteristics of traffic flow to improve the stability and accuracy of long-term predictions. For spatial modeling, a wavelet convolution-based multi-scale feature extraction mechanism is introduced to expand the effective receptive field, and a graph convolutional network is combined to model local topological relationships, thus achieving complementary spatial representations. For temporal modeling, an iTransformer encoder is used to globally model long-period temporal dependencies, and a trend-aware decomposition module explicitly separates long-term trends from periodic disturbances. Subsequently, a Mamba-based decoder utilizes a selective state-space mechanism to achieve efficient long-term dependency modeling and suppress redundant noise. Experimental results on four public benchmark datasets show that the proposed method significantly outperforms existing state-of-the-art models at different prediction step sizes, while maintaining high prediction accuracy with low computational overhead, providing an efficient and interpretable modeling framework for long-term traffic flow prediction.

Traffic prediction plays an essential role in intelligent transportation systems by supporting urban traffic management and public safety. A major challenge lies in addressing both the limitations of static assumptions and the inherent complexity they introduce when modeling dynamic and heterogeneous traffic systems. Traditional methods often simplify complex spatio-temporal data into a single-dimensional framework, potentially overlooking intricate node interactions and detailed network characteristics. This fundamental challenge manifests primarily in single-task approaches. When extended to multi-task learning scenarios, the complexity and limitations of this modeling challenge becomes more pronounced. To address these issues, this paper introduce a novel framework, Dynamic Graph Transformation with Multi-Task Learning (DGT-MTL) for spatio-temporal traffic prediction. DGT-MTL features a dynamic adjacency matrix generation module that balances static stability with dynamic flexibility. Additionally, it employs a multi-scale graph learning module to effectively capture fine-grained, latent features. An adaptive multi-task learning module is incorporated to uncover hidden correlations and dynamic relationships between road segments. Experiments conducted across six standard benchmarks demonstrate DGT-MTL's superior performance compared to contemporary approaches, achieving over 15 % improvements in both ROC-AUC and F1 score metrics. Further experiments demonstrate its effectiveness and robustness in handling complex traffic prediction.

Traffic accident prediction serves as a cornerstone of intelligent transportation systems, enabling proactive city‐wide control strategies and public safety interventions. Effective models must capture the evolving spatiotemporal propagation of risk while addressing heterogeneous data distributions across urban regions. Current approaches face significant limitations: fixed graph topologies fail to represent nonstationary accident patterns, while uniform task weighting leads to optimization bias toward data‐rich areas, ultimately constraining adaptability in adjacency construction and multihop spatial reasoning. To address these challenges, we propose a dynamic multidiffusion graph network with multitask learning (DiffG‐MTL) for city‐scale accident prediction. Specifically, a dynamic diffusion adjacency generation (DDAG) module constructs time‐varying, diffusion‐based adjacency matrices through multiple propagation pathways. A multiscale graph structure learning (MGSL) module captures multihop spatial relationships and temporal cues, while effectively highlighting anomalous traffic behaviors. To alleviate regional data imbalance, we introduce a dynamic multitask learning objective that adaptively redistributes learning focus using recall‐aware weighting and task‐level normalization. Comprehensive evaluations on six widely used datasets demonstrate that DiffG‐MTL consistently outperforms state‐of‐the‐art baselines across multiple evaluation metrics. Additional experiments validate its robustness and effectiveness in modeling complex spatiotemporal accident patterns.

The development of intelligent driving systems has drawn significant attention to enhancing the safety of autonomous vehicles and their intended functionality. Despite this, current accelerated testing approaches remain inadequate in assessing system reliability, as they fail to simulate scenarios involving collisions between vehicles and pedestrians and identify unknown risks. To address these limitations, scenario-based testing methods have been proposed, which seek to identify critical scenarios with a high frequency of exposure to safety risks. A comprehensive review of these methods is thus of paramount significance. In this article, we provide a timely and systematic literature review of existing accelerated testing for autonomous vehicles. We propose a taxonomy of these methods, discuss each subfield, and highlight open problems and future directions. Our objective is to provide a clear and concise overview of the state of the art in this field and to offer insights into the effectiveness of scenario-based testing approaches. By doing so, we aim to facilitate the identification of critical scenarios and the assessment of risk exposure frequencies, which are essential for enhancing the safety and reliability of autonomous vehicles.

Driving behavior classification is an important component of advanced driver assistance system (ADAS) and plays a pivotal role in enhancing driving safety and economy. Existing driving behavior classification methods primarily rely on time-domain features or multimodal fusion techniques. However, these methods often involve many parameters and complex training processes, making them difficult to be directly applied in real-world scenarios. This study proposes a lightweight driving behavior classification method that integrates both time-domain and frequency-domain feature. The proposed method comprises two parallel branches. The first branch extracts time-domain features using a combination of Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory Network (BiLSTM). Instead of the conventional fully connected layers, the Kolmogorov-Arnold Network (KAN) is employed to reduce modeling complexity and enhance performance. The second branch captures frequency-domain features through the Discrete Fourier Transform (DFT) method. An adaptive filtering block removes high-frequency noise, and the frequency-domain features are then fused adaptively using two learnable filters—global and denoising. The time-frequency domain multimodal feature fusion module performs the final weighted fusion of these features. Extensive experiments are conducted to evaluate the proposed method based on the UAH-DriveSet. Experimental results show that our method achieves an F1 score of 98.74%, surpassing the current state-of-the-art. Moreover, to assess the robustness of our method, experiments are conducted on the Ford Stay Alert Challenge dataset, obtaining an F1 score of 97.83%. Furthermore, our method is highly efficient, with only 3.97M parameters and 0.38G FLOPS, significantly lower than existing methods. The inference speed reaches an impressive 1,257 FPS, meeting the real-time requirements in resource-constrained environments.

The perception system is a critical element of autonomous driving, where real-time and accurate segmentation of drivable areas and lanes is essential for intelligent decision-making during vehicle operation. Current approaches primarily focus on minimizing background interference in images, often overlooking the importance of edge information. In response, this paper introduces the edge-guided multi-loss network (EdgeUNet) designed for drivable area and lane segmentation. EdgeUNet employs an encoder for feature extraction and a decoder specifically tailored for the segmentation tasks. The decoder incorporates a novel feature fusion module (FFM), multi-scale feature aggregation module (MFSA), edge extraction module (EEM), and edge-aware optimization module (EAO), facilitating efficient extraction and supervision through edge information. Our model demonstrates superior performance on the Berkeley deep drive (BDD100K) dataset, achieving state-of-the-art results with 99.3% mean pixel accuracy (mPA) and 54.8% mean intersection-over-union (MIoU) in the lane detection task. Additionally, ablation studies conducted on the TuSimple and KITTI datasets further validate the effectiveness and generalizability of EdgeUNet.

The trend of progressive technology scaling makes the computing system more susceptible to soft errors. The most critical issue that soft error incurs is silent data corruption (SDC) since SDC occurs silently without any warnings to users. Estimating SDC probability of a program is the first and essential step towards designing protection mechanism. Prior work suffers from prediction inaccuracy since the proposed heuristic-based models fail to describe the semantic of fault propagation. We propose a novel approach SLOGAN which transfers the prediction of SDC probability into a graph regression task. A program is represented in the form of dynamic dependence graph. To capture the rich semantic of fault propagation, we apply structured graph attention network, which includes node-level, graph-level and layer-level self-attention. With the learned attention coefficients from node-level, graph-level, and layer-level self-attention, the importance of edges, nodes, and layers to the fault propagation can be fully considered. We generate the graph embedding by weighted aggregation of the embeddings of nodes and compute the SDC probability by the regression model. The experiment shows that SLOGAN achieves higher SDC accuracy than state-of-the-art methods with a low time cost.

Path inference aims to reveal missing paths given a few number of GPS samples associated with a moving object by exploiting the topology of road network and statistical information of historical GPS trajectories, and plays a vital role in data preprocessing of location based information services. But, in practice path inference severely suffers from the data sparsity as well as the randomness of drivers path selection behaviors. In this paper, we propose a novel Bayesian path inference model subject to spatiotemporal constraints by taking into account the drivers path selection behaviors. To be specific, the problem of path inference is cast as the problem of searching K most probable candidate paths according to the joint posterior selection probabilities of candidate paths. When estimating model parameters, we use the frequency of each road segment in the historical GPS trajectories instead of that of road segment transfers to mitigate the influence of data sparsity. In addition, both spatiotemporal constraints and probability thresholds are introduced to narrow the search space, which significantly improves the time efficiency. The experiments are conducted using practical data and show that the proposed model is significantly superior to three existing popular models. When the GPS sampling interval varies from 1 minute to 5 minutes, the accuracy of the proposed method is 0.94, 0.91, 0.86, 0.80 and 0.74, and the Jaccard similarity 0.89, 0.85, 0.83, 0.80 and 0.75 respectively, the average improvement in accuracy rises from 3.68% to 18.69% and that in the Jaccard similarity from 4.56% to 18.42%.

In ride matching, the search results can be personalized for a particular driver. Given a query with trip plans, it is advantageous to rank potential riders in terms of who are most appealing to the driver for increasing occupancy rates. While personalized ranking approaches such as collaborative filtering and factorization are available, they are not suitable for pooling because candidate riders are associated with different preferences, and their travel is sparsely distributed with a long tail of users for a few popular destinations. The user embedding method is a good candidate in terms of alleviating data sparsity, but it has issues such as difficulty encoding user preferences from rich information. In this study, we explore user embedding techniques for the purposes of short-term personalized rider ranking, where the aim is to present to drivers a set of potential riders who share similar itineraries with them and can be picked up on their current route. Considering trip requests, along with the preferences issued in advance, this study uses attribute representations to rank the riders based on the higher-order similarities in the participants’ itineraries in a three-step manner: (i) start with a distributed representation of the riders’ preference regarding the cost of extra distance, (ii) generate user embeddings in a heterogeneous network with the meeting points and associated waiting times, and (iii) match and rank riders for drivers depending on an attribute fusion operation by adopting a personal route and schedule. Our proposed method performs well in an offline estimation on a huge dataset from DiDi in Chengdu, China. Experimental results indicate that with the learned embeddings, we can obtain statistically significant advancements (e.g., 4.6–29.5% increase in mean reciprocal rank (MRR); 2.8–17.4% in normalized discounted cumulative gain (nDCG)) over current methods for pooling ranking. Furthermore, we implement the proposed method on our simulated pooling system. These results validate that personalized ranking can undoubtedly boost the number of trips served, and reduce the total trip distance and waiting time.

Trajectory clustering is an important task in trajectory data mining. Grouping a trajectory dataset into clusters based on the similarity between vehicle trajectories is conducive to revealing the movement pattern of the vehicles. For trajectory clustering in a real urban road network environment, the existing methods have some deficiencies, including high time complexity in measuring the distance between trajectory sequences and poor clustering performance. This paper proposes a method for clustering vehicle traj ectories in the urban road network environment based on a word vector model. First, the original Global Positioning System (GPS) trajectory data of vehicles are converted into road segment sequences by using the urban road segment information contained in the GPS trajectory data (after map-matching). Then, the spatial properties, such as the geographical location, time and moving direction of vehicles in the original trajectory sequence, are expressed by road segments and their order. Next, the road segment sequences are converted into traj ectory segment eigenvectors with fixed dimensions using the doc2vec model, in this way, the calculation efficiency of similarity between traj ectory segments of different lengths is improved. Finally, the vehicle tracks are clustered according to the distances between the traj ectory segment eigenvectors using the hierarchical clustering method. The result of a simulation based on taxi trajectory data gathered in a real urban road network shows that the proposed method was superior to the traditional clustering methods based on trajectory space-time distance, improving the silhouette index by 10%-25%, and reducing the clustering time by two orders of magnitude.

Soft errors can lead to silent data corruption (SDC), seriously compromising the reliability of a system. To detect SDC, a profiling of SDC-causing instructions is usually needed to decide which instructions to protect. Current approaches gain SDC-causing instructions by using machine learning algorithms. Most of existing algorithms suffer from a lack of accuracy. Researchers choose certain structural features as input based on their understanding of fault propagation. Such hand-tuned features prevent models from reproducing the reasoning of fault propagation and that, in turn, limits their ability to make good prediction decisions. We propose GATPS, which is a Graph Attention neTwork to Predict SDC-causing instructions. The task of SDC prediction is converted into node classification in a heterogenous graph, which applies different types of edges to represent different instruction relations. Low dimensional embedding of each node is computed by attending over its neighbors through multiple types of edges. The hidden structural features related to SDC propagation can be captured automatically. To quantify fault effects between instructions, attention mechanism is applied to assign different importance to nodes of a same neighborhood. Experimental results show that GATPS improves F1 score of SDC prediction by 11.0 %-21.7 % over most competitive machine learning methods.

Recently, vehicular ad hoc networks (VANETs) got much popularity and are now being considered as integral parts of the automobile industry. As a subclass of MANETs, the VANETs are being used in the intelligent transport system (ITS) to support passengers, vehicles, and facilities like road protection, including misadventure warnings and driver succor, along with other infotainment services. The advantages and comforts of VANETs are obvious; however, with the continuous progression in autonomous automobile technologies, VANETs are facing numerous security challenges including DoS, Sybil, impersonation, replay, and related attacks. This paper discusses the characteristics and security issues including attacks and threats at different protocol layers of the VANETs architecture. Moreover, the paper also surveys different countermeasures.

In this paper, we aim to provide an optimal passenger matching solution by recommending ridesharing groups of passengers from GPS trajectories. Existing algorithms for rider grouping usually rely on matching pre-selected origin-destination coordinates. Unfortunately, the semantics in the spatial layout (e.g., social interactions and properties of the locations) are ignored, leading to inaccuracies in discovering the ridesharing groups. Meanwhile, the destinations manually entered by users impact the accuracy of matching, as these addresses are usually not available in a road network or are not optimal for passenger pickup. This is particularly true when a passenger travels in a less familiar place. Given a set of passengers and the distribution of their destination, our approach is to compute the ridesharing matching between passengers. The raw GPS trajectories can be characterized by a combination of time constraints, traffic environments, and social activities. We first developed a PrefixSpan-prediction using a partial matching (P-PPM) destination-prediction algorithm to mine the frequent movement patterns from the trajectory data and determine the confidence of the movement rules. Our method uses the total travel time as the matching objective. Our approach is superior to the baseline methods in terms of accuracy (increased from 46% to 80%). We have also achieved significant improvements on other metrics, such as users' saved travel distance. We demonstrated that using our proposed method, a group of passengers could save over 19% of total travel miles, which shows that the ridesharing scheme could be effective.

Ridesharing has attracted increasing attention in recent years, and combines the flexibility and speed of private cars with the reduced cost of fixed-line systems to benefit alleviating traffic pressure. A major issue in ridesharing is the accurate assignment of passengers to drivers, and how to maximize the number of rides shared between people being assigned to different drivers has become an increasingly popular research topic. There are two major challenges facing ride-matching: scalability and sparsity. Here, we show that network embedding drives the optimal matches between drivers and riders. Contrary to existing approaches that merely depend on the proximity between passengers and drivers, we employ a heterogeneous network to learn the latent semantics from different choices in two types of ridesharing, and extract features in terms of user trajectories and sentiment. A novel framework for ridesharing, RShareForm, which encodes not only the objects but also a variety of semantic relationships between them, is proposed. This article extends the existing skip-gram model to incorporate meta-paths over a proposed heterogeneous network. It allows diverse features to be used to search for similar participants and then ranks them to improve the quality of ride-matching. Extensive experiments on a large-scale dataset from DiDi in Chengdu, China show that by leveraging heterogeneous network embedding with meta paths, RShareForm can significantly improve the accuracy of identifying the participants for ridesharing over existing methods, including both meta-path guided similarity search methods and variants of embedding methods.