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Predicting human mobility is essential for urban planning and personalized services. The problem addressed in this study is analyzing user behavior patterns and predicting their next destination. Due to the complexity and diversity of human mobility, it's necessary to study user behavior patterns from various angles and leverage diverse context information to construct prediction models. Unfortunately, most previous research often neglects personalized preferences and falls short in offering a comprehensive understanding of user behavior patterns. Furthermore, some studies have not effectively mined and utilized contextual information. To address these shortcomings, this paper introduces a novel Personalized Behavior Modeling Network (PBMN). Compared to existing methods, PBMN provides a more comprehensive modeling of user behavior and utilizes context information more extensively, enabling more accurate prediction. It models user behavior through two parallel channels, taking into account both sequential patterns and personalized preferences, while fully utilizing different contextual information. Ultimately, it generates prediction results by personalized integration of different behavior features. Specifically, PBMN employs a pair of attention-based encoders and decoders to model the overall behavior features. Additionally, it utilizes three parallel recurrent neural networks to model recent behavior features at different levels of context information. The performance of PBMN was evaluated using two real-world datasets. Experimental results demonstrate that PBMN outperforms five mainstream prediction methods concerning three commonly used evaluation metrics, emphasizing the effectiveness of PBMN [ABSTRACT FROM AUTHOR]

High-temperature equations of state (EoSs) for solid magnesium with hexagonal close-packing (hcp) and for liquid magnesium were formulated herein by using experimental data on the thermodynamic properties, thermal expansion, compressibility, temperature-dependent bulk compression modulus, and melting curve. The totality of experimental data was co-optimized using the temperature-dependent Tait EoS over a pressure range of 0–500 kbar at temperatures of 20–923 K for solid Mg and at 923–2000 K for liquid Mg. The temperature dependence of thermodynamic and thermophysical parameters was described by the extended Einstein model. The resultant EoSs give a good fit to the whole set of experimental data within measurement errors of individual quantities. The high prediction accuracy was achieved by estimating the thermodynamic and thermophysical properties of solid Mg. For liquid Mg, the suggested model adequately describes and, in some cases, averages the existing experimental data. [ABSTRACT FROM AUTHOR]

Intelligent vehicles passing through intersections in platoons have the potential to reduce speed fluctuations and improve traffic efficiency. This paper centers on the cooperative optimization of platooning behavior in urban fixed-time signalized intersection scenarios. It delves into the spatial group distribution characteristics of platoons within an intersection entrance lane and employs modeling techniques to vividly portray and express group behaviors, including lane-changing and car-following, across various physical areas within a platoon. Based on real-world traffic data collected in Beijing, SUMO (Simulation of Urban Mobility) is used to build simulation scenarios in this research. Considering both low- and high-volume conditions, the influence of different platoon size combinations on entrance lane traffic efficiency improvement is explored by adjusting the platoon size to determine an ideal vehicle group organization form under a given fixed signal timing. Experimental results show that the optimal platoon sizes and crossing sequences are affected by traffic volume and the number of lanes. For example, when the volume of a single lane in the east entrance straight direction is large (500 or 600 pcu/h/lane), the observed optimal platoon size is 7 to 9 pcu/p (pcu/platoon). These findings indicate that at fixed-time signalized intersections, there exists a reasonable platoon size that optimizes the overall capacity of each entrance, which provides ideas for future vehicle group control. [ABSTRACT FROM AUTHOR]

In traditional traffic simulation studies, vehicle behavior has typically been modeled using complex analytical frameworks, which often struggle to encompass the full range of variables affecting vehicle operations. Addressing this gap, our research introduces an innovative data-driven framework for traffic simulation that incorporates human driving data into its decision-making processes. This enables the modeling of diverse vehicle behaviors by taking into account both vehicle-specific characteristics and environmental factors. At the core of this framework are two advanced deep neural networks, convolutional long short-term memory and convolutional gated recurrent unit, which underpin our vehicle traffic simulation model. Utilizing datasets from the Next Generation Simulation project, specifically the I-80 and US-101 road sections, our study further evaluates the framework's performance through single-step continuous prediction, as well as transferability tests, employing the TransMSEloss function to optimize prediction accuracy. Our findings reveal that the proposed data-driven model significantly outperforms traditional models, achieving an exceptional accuracy of 97.22% in training and 95.76% in testing. Notably, in continuous prediction, our model maintains an 89.57% accuracy up to the fifth step, exceeding the traditional framework's 82.82% by 5% to 10% at each step. Time cost analysis indicates that while the data-driven framework's advantages are more pronounced in large-scale simulations, it also demonstrates strong transferability, with a 93.48% accuracy on diverse datasets, showcasing its applicability across different traffic scenarios. This study not only highlights the potential of deep learning in traffic simulation, but also sets a new benchmark for accuracy and scalability in the field. [ABSTRACT FROM AUTHOR]

A high-temperature equation of state (EoS) for alpha-alumina (corundum) with a hexagonal close-packed (hcp) lattice was derived herein using experimental data on the thermodynamic properties, thermal expansion, compressibility and temperature-dependent bulk compression modulus. The experimental data were co-optimized using the temperature-dependent Tait equation over a pressure range from 0 to 1677 kbar and over a temperature range from 20 to 2327 K. The temperature dependence of thermodynamic and thermophysical parameters was fitted by an extended Einstein model. For the Tait equation, the isothermal bulk compression modulus and its pressure derivative at 0 K were estimated to be 2570.3 kbar and 4.1944, respectively. The temperature derivative of the bulk compression modulus was estimated to be 2.10·10–4 K–1. The obtained EoS provides a good fit to the whole set of the experimental data within the measurement error of individual parameters. [ABSTRACT FROM AUTHOR]

Autonomous vehicles are advanced driving systems that revolutionize transportation, but their vulnerability to adversarial attacks poses significant safety risks. Consider a scenario in which a slight perturbation in sensor data causes an autonomous vehicle to fail unexpectedly, potentially leading to accidents. Current testing methods often rely on computationally expensive active learning techniques to identify such vulnerabilities. Rather than actively training complex adversaries by interacting with the environment, there is a need to first intelligently find and reduce the search space to only those states where autonomous vehicles are found to be less confident. In this paper, we propose a black-box testing framework ReMAV that uses offline trajectories first to efficiently identify weaknesses of autonomous vehicles without the need for active interaction. To this end, we introduce a three-step methodology which i) uses offline state action pairs of any autonomous vehicle under test, ii) builds an abstract behavior representation using our designed reward modeling technique to analyze states with uncertain driving decisions, and iii) uses a disturbance model for minimal perturbation attacks where the driving decisions are less confident. Our reward modeling creates a behavior representation that highlights regions of likely uncertain autonomous vehicle behavior, even when performance seems adequate. This enables efficient testing without computationally expensive active adversarial learning. We evaluated ReMAV in a high-fidelity urban driving simulator across various single- and multi-agent scenarios. The results show substantial increases in failure events compared to the standard behavior of autonomous vehicles: 35% in vehicle collisions, 23% in road object collisions, 48% in pedestrian collisions, and 50% in off-road steering events. ReMAV outperforms two baselines and previous testing frameworks in effectiveness, efficiency, and speed of identifying failures. This demonstrates ReMAV’s capability to efficiently expose autonomous vehicle weaknesses using simple perturbation models.

The issues of both system security and safety can be dissected integrally from the perspective of behavioral appropriateness. That is, a system that is secure or safe can be judged by whether the behavior of certain agent(s) is appropriate or not. Specifically, a so-called appropriate behavior involves the right agent performing the right actions at the right time under certain conditions. Then, according to different levels of appropriateness and degrees of custodies, behavioral authentication can be graded into three levels, i.e., the authentication of behavioral Identity, Conformity, and Benignity. In a broad sense, for the security and safety issue, behavioral authentication is not only an innovative and promising method due to its inherent advantages but also a critical and fundamental problem due to the ubiquity of behavior generation and the necessity of behavior regulation in any system. By this classification, this review provides a comprehensive examination of the background and preliminaries of behavioral authentication. It further summarizes existing research based on their respective focus areas and characteristics. The challenges confronted by current behavioral authentication methods are analyzed, and potential research directions are discussed to promote the diversified and integrated development of behavioral authentication. [ABSTRACT FROM AUTHOR]