Real Trajectory Research Articles

Observer-based fuzzy T–S control with an estimation error guarantee for MPPT of a photovoltaic battery charger in partial shade conditions

To improve the efficiency and performance of a photovoltaic system (PV) an observer-based fuzzy controller design methodology is provided in the study. The desired controller is achieved by employing a combination of linear matrix inequalities (LMIs). The system consists of a photovoltaic generator (PVG) connected to a boost converter. A battery is linked to the boost converter to stock additional energy for further use. A fuzzy controller based on a T–S fuzzy type observer that guarantees a predefined L2 performance is suggested to achieve maximum power point tracking (MPPT) even under changing weather conditions. An optimal trajectory should be tracked to ensure maximum power operation. For this aim, a specific reference fuzzy model is proposed to create the aimed trajectories. Using this method, the system dynamics are precisely reproduced over a large range of operations. The whole T–S fuzzy methodology, suggested in this paper, aims to ensure the most efficient energy recovery to recharge a battery under partially shaded conditions, resulting in high system efficiency. The proposed method is simulated with MATLAB/SIMULINK and the simulation results, with realistic reference trajectories, are driven while taking into account climate variations. The analysis of these simulations, along with a comparison with two other commonly used approaches, led to the conclusion that the suggested strategy succeeded in reducing the tracking time, as well as eliminating the oscillation that often occurs around maximum power point (MPP).

Integrating the lifelong exposure dimension of a chemical mixture into the risk assessment process. Application to trace elements.

Lifelong, the general population is exposed to mixtures of chemicals. Most often, risk assessment is performed to estimate the probability of adverse effects in the population using external exposures to a single chemical and considering one route of exposure. To estimate whole exposure to a chemical, human biomonitoring studies are used to measure chemical concentrations in biological matrices. The limitations of these studies are that is not possible to distinguish the sources or the routes of exposure. Moreover, only the concentrations of a limited number of chemicals are usually determined due to the associated cost. In this study, a methodology has been developed to estimate the internal exposures of the population to a mixture of trace elements (inorganic As, Cd, Pb and Hg) throughout lifetime. This methodology uses realistic lifetime exposure trajectories coupled to physiological based kinetic modeling, considering several sources of exposure. Then, the estimated biomarkers of exposure were compared to human biomonitoring data to estimate the robustness of the methodology. Finally, risk characterization was performed based on the simulated biomarkers of exposure considering an additive effect of chemicals. This methodology allows to determine the contribution of chemicals to the overall risk of renal effect.

Experimental Visualization and Modeling of the Transport Behaviors of Monofilament Microplastic Fibers Through an Idealized Porous Media

AbstractMicroplastic fibers (MPF) are the largest fraction of microplastics in the environment by mass. The endpoints of these contaminants' movement is generally known at large‐scale (i.e., their origins and where they end up), but the mechanics of how they get to those sinks remains poorly understood. The objective of this work was to improve understanding of the mechanisms driving MPF migration through terrestrial systems by directly imaging their motion through idealized representations of porous media. Monofilament line with 0.3 mm diameter was passed through a bench‐scale, pseudo‐2d flow cell to capture trajectories of MPFs of three different lengths and trajectories of passive micro‐bead tracers were also captured. Video processing and automated image analysis converted the video of the experiments into a database of trajectories, allowing comparison of the experimental data to various numerical models. Simple advection‐dispersion models were adequate for modeling the passive tracer but did not provide a good description of MPF transport. A physics‐based, distributed model was able to generate realistic trajectories through the domain, but the speeds of the fibers in the initial simulation were too fast, despite working well for the passive tracer. Adding a delay (waiting time) process resulted in good description of the trajectories and travel times. The specifics of the delay process could not be deduced from these experiments, but its overall impact on transport provides mechanistic insights. These direct observation of the trajectories and speeds of MPFs moving through porous media show that MPFs likely have strong interactions with their surroundings.

Analysis of the Dynamics of the Lower Drill String Assembly in a Deep Well Coring Operation

In order to carry out the geological characteristics, evolution law and other scientific research of the deep strata in the extra-deep well, it is necessary to carry out the coring operation in the extra-deep depth. However, with the increase of drilling depth, the force of drill string in the underground becomes more and more complicated, and there is contact collision between the core and the core after the core tool is bent, which makes the core broken. Clarifying the dynamic behavior of the BHA during the extra-deep well coring process is helpful to obtain a more complete core. Therefore, this paper adopts the simulation analysis method to simulate the coring process of the extra-deep well with a 163.5mm diamond bit and a 140mm corer under the real drilling hole trajectory. The stress, eddy motion and the contact between the inner barrel of the core tool and the core of the lower 1000m drill assembly are analyzed. The results show that the hole expansion rate of coring bit is 2.5% in the process of low bit weight and low rotational speed. When the core is 8m, the core barrel is under compression, bending and torsion, and under the restriction of the centralizer and the core hole, the middle and upper part of the core tool bend greatly, and the contact degree with the core is high, and the larger contact force is easy to cause the lower core to fracture.

GLMI: An Efficient Spatiotemporal Index Leveraging Geohash and Piecewise Linear Models for Optimized Query Performance

Spatiotemporal big data contain information in multiple dimensions such as space and time. Spatiotemporal data have the characteristics of large volume, intricate spatiotemporal relationship, and uneven spatiotemporal distribution. Index structure is one of the most important technologies used to improve system data analysis and workload. However, it is difficult to dynamically adjust with data density, resulting in increased maintenance costs and retrieval complexity. At the same time, maintaining the proximity of spatiotemporal data in spatial or temporal dimensions is crucial for efficient spatiotemporal analysis. To address these challenges, this paper proposes a learned index method, GLMI (Geohash and piecewise linear model-based index for spatiotemporal data). GLMI uses dynamic space partitioning based on the Hilbert curve to reduce the impact of data skew on index performance. In the time dimension, a piecewise linear model was constructed using the ShrinkingCone algorithm, and a buffer was designed to support the fast writing of spatiotemporal data. Compared with the current mainstream traditional high-dimensional indexes and the ZM index, GLMI has a smaller space consumption and shorter construction time compared to high-dimensional learned indexes on real traffic itinerary and trajectory record datasets. Meanwhile, GLMI also has an advantage in query efficiency.

Secure and Efficient k-anonymous Trajectory Privacy Protection Method based on Differential Privacy

The Location-based service scheme have already involved in every aspect of People's daily life and are increasingly used in various industries. Aiming at the problem of the security and efficiency of mobile terminal users’ trajectory privacy protection in location-based service, we propose a k-anonymous trajectory privacy protection scheme based on differential privacy. This scheme adopts differential privacy technology to add Laplace noise to the user's trajectory many times to generate 2k noise trajectory, and then according to the trajectory similarity to determine k-1 noise users whose trajectory are similar to the user trajectory, and sets them and the real user as an anonymous user group, and then uses the anonymous user group to request LBS services. Security analysis shows that the scheme satisfies the security features of anonymity, unforgeability, and anti-counterfeiting attack. The simulation results show that the scheme not only guarantees the similarity between the false trajectory and the real trajectory but also has higher execution efficiency.

Multi-objective optimization for connected and automated truck platoon control with improved CACC model

Connected and Automated Truck Platoon (CATP) refers to a group of trucks traveling closely together with minimal spacing to improve fuel economy and safety. However, challenges arise from instability due to internal platoon factors and external traffic disturbances. This research presents an improved Cooperative Adaptive Cruise Control (CACC) model tailored for CATP to address these challenges. The model is designed to enhance safety, fuel efficiency, and traffic efficacy. The improvements of the proposed model are in two aspects: the optimizing of the time headway strategy and the dynamic parameter adjustments of controller based on multi-objectives. The Dynamic Safety Requirement Time Headway (DSRTH) strategy facilitates the timely detection of the accelerations of the leading vehicles within the platoon, enabling quick driving responses. Additionally, Model Predictive Control (MPC) enables dynamic calibration of Proportional-Derivative (PD) control parameters and issuance of velocity commands. Meanwhile, the integration of a second-order time-delay response model has been implemented to adapt to dynamic changes in commands. A transfer function has been established, and stability has been proven. To evaluate the model performance, simulation analysis was performed using real vehicle trajectories as the CATP following vehicles. The results indicate that the DSRTH strategy outperforms both the Constant Time Headway (CTH) and Variable Time Headway (VTH) strategies, allowing rear vehicles to reach the speed trough earlier, with response speeds improved by 3.1 % and 1.5 %, respectively. Compared to the Intelligent Driver Model (IDM) and CACC models, the improved CACC model achieves a steady state of constant acceleration sooner, with recovery times reduced by 17.7 % and 3.2 %. Additionally, compared to the IDM model, the improved CACC model can save 3.23 % in fuel consumption. Furthermore, sensitivity analysis indicates that as the CATP proportion and platoon size increase, there is a positive impact on traffic flow. However, when the platoon size exceeds 5 vehicles, it shows a negative impact on the stability of other vehicles in the traffic flow besides those in the CATP.

A Novel Trajectory Prediction Method Based on CNN, BiLSTM, and Multi-Head Attention Mechanism

A four-dimensional (4D) trajectory is a multi-dimensional time series that embodies rich spatiotemporal features. However, its high complexity and inherent uncertainty pose significant challenges for accurate prediction. In this paper, we present a novel 4D trajectory prediction model that integrates convolutional neural networks (CNNs), bidirectional long short-term memory networks (BiLSTMs), and multi-head attention mechanisms. This model effectively addresses the characteristics of aircraft flight trajectories and the difficulties associated with simultaneously extracting spatiotemporal features using existing prediction methods. Specifically, we leverage the local feature extraction capabilities of CNNs to extract key spatial and temporal features from the original trajectory data, such as geometric shape information and dynamic change patterns. The BiLSTM network is employed to consider both forward and backward temporal orders in the trajectory data, allowing for a more comprehensive capture of long-term dependencies. Furthermore, we introduce a multi-head attention mechanism that enhances the model’s ability to accurately identify key information in the trajectory data while minimizing the interference of redundant information. We validated our approach through experiments conducted on a real ADS-B trajectory dataset. The experimental results demonstrate that the proposed method significantly outperforms comparative approaches in terms of trajectory estimation accuracy.

An in vitro study of a combined patient-specific device for safe and accurate insertion of infrazygomatic crest miniscrews.

To develop and assess the efficacy of a novel combined patient-specific device (CPSD) for the accurate and safe insertion of infrazygomatic crest miniscrews in orthodontic procedures. Twenty-eight miniscrews were placed in the infrazygomatic crest region of 28 cadaver maxillae using the direct manual method (n = 14) or the CPSD (n = 14) based on preset trajectories. The CPSD, designed based on the integration model, included a positioning guide, an insertion guide, and a depth-limiting groove. Deviations in the insertion site, tip location, insertion angle, and biting depth between the preset and real insertion trajectories were calculated to evaluate the accuracy of miniscrew insertion. Classification frequencies of root proximity, sinus penetration depth, and biting depth of the miniscrew after insertion were also calculated to evaluate the safety of miniscrew insertion. Regarding evaluation of accuracy, significant differences were observed in the deviation values of the insertion site, tip location, insertion angle, and biting depth between the CPSD and freehand groups (P = .001, P < .001, P < .001, P = .039, respectively). Regarding evaluation of safety, a significant difference was observed in the classification frequencies of root proximity between the two groups (P = .016). Compared with manual insertion, CPSD could be a preferred method for safe and accurate insertion of infrazygomatic crest miniscrews for orthodontists.

Trajectory privacy preservation model based on LSTM-DCGAN

Rapid scientific and technological development has brought many innovations to electronic devices, which has greatly improved our daily lives. Nowadays, many apps require the permission to access user location information, causing the concern on user privacy and making it an important task to protect user trajectory information. This paper proposes a novel model called LSTM-DCGAN by integrating LSTM (Long Short-Term Memory Network) with DCGAN (Deep Convolution Generative Adversarial Network). LSTM-DCGAN takes the advantages of LSTM to remember attributes in the trajectory data and the generator and the discriminator in DCGAN to generate and discriminate the trajectories. The proposed model is trained using real user trajectory data and the experimental results are validated from the perspectives of both effectiveness and practicality. Results show that the proposed LSTM-DCGAN model outperforms similar methods in generating synthesized trajectories that are similar to real trajectories in terms of the temporal and the spatial characteristics. In addition, various influencing factors are evaluated to investigate ways of further improving and optimizing the model. Overall, the proposed LSTM-DCGAN model can achieve the balance between the effectiveness of privacy protection and the practicality of user trajectory data and can thus be applied to safeguarding user trajectory information.

AstroSLAM: Autonomous monocular navigation in the vicinity of a celestial small body—Theory and experiments

We propose AstroSLAM, a standalone vision-based solution for autonomous online navigation around an unknown celestial target small body. AstroSLAM is predicated on the formulation of the SLAM problem as an incrementally growing factor graph, facilitated by the use of the GTSAM library and the iSAM2 engine. By combining sensor fusion with orbital motion priors, we achieve improved performance over a baseline SLAM solution and outperform state-of-the-art methods predicated on pre-integrated inertial measurement unit factors. We incorporate orbital motion constraints into the factor graph by devising a novel relative dynamics—RelDyn—factor, which links the relative pose of the spacecraft to the problem of predicting trajectories stemming from the motion of the spacecraft in the vicinity of the small body. We demonstrate AstroSLAM’s performance and compare against the state-of-the-art methods using both real legacy mission imagery and trajectory data courtesy of NASA’s Planetary Data System, as well as real in-lab imagery data produced on a 3 degree-of-freedom spacecraft simulator test-bed.

GSTGM: Graph, spatial–temporal attention and generative based model for pedestrian multi-path prediction

Pedestrian trajectory prediction in urban environments has emerged as a critical research area with extensive applications across various domains. Accurate prediction of pedestrian trajectories is essential for the safe navigation of autonomous vehicles and robots in pedestrian-populated environments. Effective prediction models must capture both the spatial interactions among pedestrians and the temporal dependencies governing their movements. Existing research primarily focuses on forecasting a single trajectory per pedestrian, limiting its applicability in real-world scenarios characterised by diverse and unpredictable pedestrian behaviours. To address these challenges, this paper introduces the Graph Convolutional Network, Spatial–Temporal Attention, and Generative Model (GSTGM) for pedestrian trajectory prediction. GSTGM employs a spatiotemporal graph convolutional network to effectively capture complex interactions between pedestrians and their environment. Additionally, it integrates a spatial–temporal attention mechanism to prioritise relevant information during the prediction process. By incorporating a time-dependent prior within the latent space and utilising a computationally efficient generative model, GSTGM facilitates the generation of diverse and realistic future trajectories. The effectiveness of GSTGM is validated through experiments on real-world scenario datasets. Compared to the state-of-the-art models on benchmark datasets such as ETH/UCY, GSTGM demonstrates superior performance in accurately predicting multiple potential trajectories for individual pedestrians. This superiority is measured using metrics such as Final Displacement Error (FDE) and Average Displacement Error (ADE). Moreover, GSTGM achieves these results with significantly faster processing speeds.

Surrogate-Based Multidisciplinary Optimization for the Takeoff Trajectory Design of Electric Drones

Electric vertical takeoff and landing (eVTOL) aircraft attract attention due to their unique characteristics of reduced noise, moderate pollutant emission, and lowered operating cost. However, the benefits of electric vehicles, including eVTOL aircraft, are critically challenged by the energy density of batteries, which prohibit long-distance tasks and broader applications. Since the takeoff process of eVTOL aircraft demands excessive energy and couples multiple subsystems (such as aerodynamics and propulsion), multidisciplinary analysis and optimization (MDAO) become essential. Conventional MDAO, however, iteratively evaluates high-fidelity simulation models, making the whole process computationally intensive. Surrogates, in lieu of simulation models, empower efficient MDAO with the premise of sufficient accuracy, but naive surrogate modeling could result in an enormous training cost. Thus, this work develops a twin-generator generative adversarial network (twinGAN) model to intelligently parameterize takeoff power and wing angle profiles of an eVTOL aircraft. The twinGAN-enabled surrogate-based takeoff trajectory design framework was demonstrated on the Airbus A3 Vahana aircraft. The twinGAN provisioned two-fold dimensionality reductions. First, twinGAN generated only realistic trajectory profiles of power and wing angle, which implicitly reduced the design space. Second, twinGAN with three variables represented the takeoff trajectory profiles originally parameterized using 40 B-spline control points, which explicitly reduced the design space while maintaining sufficient variability, as verified by fitting optimization. Moreover, surrogate modeling with respect to the three twinGAN variables, total takeoff time, mass, and power efficiency, reached around 99% accuracy for all the quantities of interest (such as vertical displacement). Surrogate-based, derivative-free optimizations obtained over 95% accuracy and reduced the required computational time by around 26 times compared with simulation-based, gradient-based optimization. Thus, the novelty of this work lies in the fact that the twinGAN model intelligently parameterized trajectory designs, which achieved implicit and explicit dimensionality reductions. Additionally, twinGAN-enabled surrogate modeling enabled the efficient takeoff trajectory design with high accuracy and computational cost reduction.

Generative diffusion models for synthetic trajectories of heavy and light particles in turbulence

Heavy and light particles are commonly found in many natural phenomena and industrial processes, such as suspensions of bubbles, dust, and droplets in incompressible turbulent flows. Based on a recent machine learning approach using a diffusion model that successfully generated single tracer trajectories in three-dimensional turbulence and passed most statistical benchmarks across time scales, we extend this model to include heavy and light particles. Given the particle type – tracer, light, or heavy – the model can generate synthetic, realistic trajectories with correct fat-tail distributions for acceleration, anomalous power laws, and scale dependent local slope properties. This work paves the way for future exploration of the use of diffusion models to produce high-quality synthetic datasets for different flow configurations, potentially allowing interpolation between different setups and adaptation to new conditions.

Geometry of transcendental singularities of complex analytic functions and vector fields

Abstract On Riemann surfaces M M , there exists a canonical correspondence between a possibly multivalued function Ψ X {\Psi }_{X} whose differential is single-valued (i.e. an additively automorphic singular complex analytic function) and a vector field X X . From the point of view of vector fields, the singularities that we consider are zeros, poles, isolated essential singularities, and accumulation points of the above. The theory of singularities of the inverse function Ψ X ‒ 1 {\Psi }_{X}^{&amp;#x2012;1} is extended from meromorphic functions to additively automorphic singular complex analytic functions. The main contribution is a complete characterization of when a singularity of Ψ X − 1 {\Psi }_{X}^{-1} is algebraic, is logarithmic, or arises from a zero with non-zero residue of X X . Relationships between analytical properties of Ψ X {\Psi }_{X} , singularities of Ψ X − 1 {\Psi }_{X}^{-1} and singularities of X X are presented. Families and sporadic examples showing the geometrical richness of vector fields on the neighbourhoods of the singularities of Ψ X − 1 {\Psi }_{X}^{-1} are studied. As applications, we have; a description of the maximal univalence regions for complex trajectory solutions of a vector field X X , a geometric characterization of the incomplete real trajectories of a vector field X X , and a description of the singularities of the vector field associated with the Riemann ξ {\rm{\xi }} -function.

基于扫描上下文和 NDT-ICP 融合方案的果园同步定位与绘图方法研究

Simultaneous localization and mapping (SLAM) is one of the key technologies for agricultural robots to build maps and localize in complex orchard environments and realize unmanned autonomous operations. Due to the complexity of the orchard environment, the single canopy feature and the diffuse reflection of light caused by the leaves, etc., the map construction process of the orchard environment leads to mismatch and increases the cumulative error of the map construction. Aiming at the above problems, this paper propose a navigation map construction method for orchard environment based on the fusion of Scan Context and NDT-ICP. The method firstly searches the Ring key quickly to get the candidate frames, and scores the similarity between the candidate frames and the current frame, and effectively detects the loopbacks by two-stage searching algorithm to reduce the false matches in the map of orchard environment. Meanwhile, a point cloud alignment method based on the fusion of normal distribution transform coarse alignment and iterative nearest point exact alignment is used to reduce the cumulative error of the orchard environment map. The results show that the improved algorithm compensates the drift of the point cloud map with higher mapping accuracy, better real-time performance, lower resource utilization, higher overlap between the trajectory estimation and the real trajectory, smoother loops, and a 4% reduction in CPU occupancy. In the complex orchard environment, the root mean square error and standard deviation of the trajectories of this paper's algorithm are 0.57 m and 0.19 m, which are 68% and 83% higher than those of the loop detection algorithms in the Lightweight Ground Optimized Lidar Trajectory Measurement and Multivariate Terrain Mapping (LeGO-LOAM), respectively. Accurate map construction and low drift pose estimation can be performed.The research algorithm effectively reduces the influence of mis-matching and large cumulative error in the process of map construction in the orchard environment, meets the demand for high-precision environmental mapping in the orchard environment, and provides technical support for promoting unmanned operation in the orchard environment.

Time Makes Space: Emergence of Place Fields in Networks Encoding Temporally Continuous Sensory Experiences.

The vertebrate hippocampus is believed to use recurrent connectivity in area CA3 to support episodic memory recall from partial cues. This brain area also contains place cells, whose location-selective firing fields implement maps supporting spatial memory. Here we show that place cells emerge in networks trained to remember temporally continuous sensory episodes. We model CA3 as a recurrent autoencoder that recalls and reconstructs sensory experiences from noisy and partially occluded observations by agents traversing simulated arenas. The agents move in realistic trajectories modeled from rodents and environments are modeled as continuously varying, high-dimensional, sensory experience maps (spatially smoothed Gaussian random fields). Training our autoencoder to accurately pattern-complete and reconstruct sensory experiences with a constraint on total activity causes spatially localized firing fields, i.e., place cells, to emerge in the encoding layer. The emergent place fields reproduce key aspects of hippocampal phenomenology: a) remapping (maintenance of and reversion to distinct learned maps in different environments), implemented via repositioning of experience manifolds in the network's hidden layer, b) orthogonality of spatial representations in different arenas, c) robust place field emergence in differently shaped rooms, with single units showing multiple place fields in large or complex spaces, and d) slow representational drift of place fields. We argue that these results arise because continuous traversal of space makes sensory experience temporally continuous. We make testable predictions: a) rapidly changing sensory context will disrupt place fields, b) place fields will form even if recurrent connections are blocked, but reversion to previously learned representations upon remapping will be abolished, c) the dimension of temporally smooth experience sets the dimensionality of place fields, including during virtual navigation of abstract spaces.

Transfer entropy on collective motion with undeclared loose leader–follower (LLF) structure

During an emergency or evacuation, individuals focus on the movement of their neighbors and follow those in sight, even without social affiliation, forming the undeclared Loose Leader-Follower (LLF) group, rather than the usual assumption of following certain rules in most of evacuation models. Inspired by the real-life experience and experimental data, the study develops a novel stochastic process-based evacuation model, integrated the LLF structure in collective motion to clarify the mechanism underlying individual interaction and refine the walking process according to attainable information. Transfer entropy (TE), a model-free measure of the direction of information flow, is used to detect the role of the individuals in the LLF structure from the time-series reconstructed motion information (e.g., the walking coordinate series). To reduce the error of the role detection, according to the 3σ principle for TE values, the detection threshold is decided based on the surrogate walking data. For simulation of their motion decision in the LLF structure, the TE is integrated into the walking behavior mode (TE-POMDP) using the Partially Observable Markov Decision Process (POMDP), that produces their motion behavior under the typical attained information condition. The results show that the TE-POMDP can provide more realistic trajectories. Additionally, we find that a ‘Delta-like’ formation of leading group distribution was presented and remained stable in the procession after a short period of confusion in early stage.

Large-scale deployment of intelligent transportation to help achieve low-carbon and clean sustainable transportation

Sustained deep emission reduction in road transportation is encountering bottleneck. The Intelligent Transportation-Speed Guidance System (ITSGS) is anticipated to overcome this challenge and facilitate the achievement of low-carbon and clean transportation. Here, we compiled vehicle emission datasets collected from real-world road experiments and identified the mapping relationships between four pollutants (CO2, CO, NOx, and THC) and their influencing factors through machine learning. We developed random forest models for each pollutant and achieved strong predictive performance, with an R2 exceeding 0.85 on the test dataset for all models. The environmental benefits of ITSGS at the urban scale were quantified by combining emission models with large-scale real trajectory data from Zibo, Shandong Province. Based on temporal and spatial analyses, we found that ITSGS has varying degrees of emission reduction potential during the morning peak, flat peak, and evening peak hours. Values can range from 5.71 %–8.16 % for CO2 emissions, 13.63 %–16.25 % for NOx emissions, 13.69 %–16.45 % for CO emissions, and 4.84–7.07 % for THC emissions, respectively. Additionally, ITSGS can significantly expand the area of low transient emission zones. The best time for achieving maximum environmental benefits from ITSGS is during the workday flat peak. ITSGS limits high-speed and aggressive driving behavior, thereby smoothing the driving trajectory, reducing the frequency of speed switches, and lowering road traffic emissions. The results of the ITSGS environmental benefits evaluation will provide new insights and solutions for sustainable road traffic emission reduction. SynopsisLarge-scale deployment of Intelligent Transportation - Speed Guidance System is a sustainable solution to help achieve low-carbon and clean transportation.

Neural general circulation models for weather and climate.

General circulation models (GCMs) are the foundation of weather and climate prediction1,2. GCMs are physics-based simulators that combine a numerical solver for large-scale dynamics with tuned representations for small-scale processes such as cloud formation. Recently, machine-learning models trained on reanalysis data have achieved comparable or better skill than GCMs for deterministic weather forecasting3,4. However, these models have not demonstrated improved ensemble forecasts, or shown sufficient stability for long-term weather and climate simulations. Here we present a GCM that combines a differentiable solver for atmospheric dynamics with machine-learning components and show that it can generate forecasts of deterministic weather, ensemble weather and climate on par with the best machine-learning and physics-based methods. NeuralGCM is competitive with machine-learning models for one- to ten-day forecasts, and with the European Centre for Medium-Range Weather Forecasts ensemble prediction for one- to fifteen-day forecasts. With prescribed sea surface temperature, NeuralGCM can accurately track climate metrics for multiple decades, and climate forecasts with 140-kilometre resolution show emergent phenomena such as realistic frequency and trajectories of tropical cyclones. For both weather and climate, our approach offers orders of magnitude computational savings over conventional GCMs, although our model does not extrapolate to substantially different future climates. Our results show that end-to-end deep learning is compatible with tasks performed by conventional GCMs and can enhance the large-scale physical simulations that are essential for understanding and predicting the Earth system.