3D Road Network Modeling and Road Structure Recognition in Internet of Vehicles

Internet of Vehicles (IoV) is a new system that enables individual vehicles to connect with nearby vehicles,people, transportation infrastructure, and networks, thereby realizing amore intelligent and efficient transportationsystem. The movement of vehicles and the three-dimensional (3D) nature of the road network cause the topologicalstructure of IoV to have the high space and time complexity.Network modeling and structure recognition for 3Droads can benefit the description of topological changes for IoV. This paper proposes a 3Dgeneral roadmodel basedon discrete points of roads obtained from GIS. First, the constraints imposed by 3D roads on moving vehicles areanalyzed. Then the effects of road curvature radius (Ra), longitudinal slope (Slo), and length (Len) on speed andacceleration are studied. Finally, a general 3D road network model based on road section features is established.This paper also presents intersection and road section recognition methods based on the structural features ofthe 3D road network model and the road features. Real GIS data from a specific region of Beijing is adopted tocreate the simulation scenario, and the simulation results validate the general 3D road network model and therecognitionmethod. Therefore, thiswork makes contributions to the field of intelligent transportation by providinga comprehensive approach tomodeling the 3Droad network and its topological changes in achieving efficient trafficflowand improved road safety.

Unstructured Road Extraction in UAV Images based on Lightweight Model

There is no unified planning standard for unstructured roads,and the morphological structures of these roads are complex and varied.It is important to maintain a balance between accuracy and speed for unstructured road extraction models.Unstructured road extraction algorithms based on deep learning have problems such as high model complexity,high computational cost,and the inability to adapt to current edge computing devices.Therefore,it is best to use lightweight network models.Considering the need for lightweight models and the characteristics of unstructured roads with different pattern shapes,such as blocks and strips,a TMB(Triple Multi-Block)feature extraction module is proposed,and the overall structure of the TMBNet network is described.The TMB module was compared with SS-nbt,Non-bottleneck-1D,and other modules via experiments.The feasibility and effectiveness of the TMB module design were proven through experiments and visualizations.The comparison experiment,using multiple convolution kernel categories,proved that the TMB module can improve the segmentation accuracy of the network.The comparison with different semantic segmentation networks demonstrates that the TMBNet network has advantages in terms of unstructured road extraction.

Autonomous Vehicle Platoons In Urban Road Networks:A Joint Distributed Reinforcement Learning and Model Predictive Control Approach

In this paper, platoons of autonomous vehicles operating in urban road networks are considered. From a methodological point of view, the problem of interest consists of formally characterizing vehicle state trajectory tubes by means of routing decisions complying with traffic congestion criteria. To this end, a novel distributed control architecture is conceived by taking advantage of two methodologies: deep reinforcement learning and model predictive control. On one hand, the routing decisions are obtained by using a distributed reinforcement learning algorithm that exploits available traffic data at each road junction. On the other hand, a bank of model predictive controllers is in charge of computing the more adequate control action for each involved vehicle. Such tasks are here combined into a single framework:the deep reinforcement learning output(action) is translated into a set-point to be tracked by the model predictive controller;conversely, the current vehicle position, resulting from the application of the control move, is exploited by the deep reinforcement learning unit for improving its reliability. The main novelty of the proposed solution lies in its hybrid nature: on one hand it fully exploits deep reinforcement learning capabilities for decisionmaking purposes;on the other hand, time-varying hard constraints are always satisfied during the dynamical platoon evolution imposed by the computed routing decisions. To efficiently evaluate the performance of the proposed control architecture, a co-design procedure, involving the SUMO and MATLAB platforms, is implemented so that complex operating environments can be used, and the information coming from road maps(links,junctions, obstacles, semaphores, etc.) and vehicle state trajectories can be shared and exchanged. Finally by considering as operating scenario a real entire city block and a platoon of eleven vehicles described by double-integrator models, several simulations have been performed with the aim to put in light the main f eatures of the proposed approach. Moreover, it is important to underline that in different operating scenarios the proposed reinforcement learning scheme is capable of significantly reducing traffic congestion phenomena when compared with well-reputed competitors.

System Identification and Parameter Self-Tuning Controller on Deep-Sea Mining Vehicle

System identification is a quintessential measure for real-time analysis on kinematic characteristics for deep-sea mining vehicle, and thus to enhance the control performance and testing efficiency. In this study, the system identification algorithm, recursive least square method with instrumental variables(IV-RLS), is tailored to model ‘Pioneer I’, a deep-sea mining vehicle which recently completed a 1305-meter-deep sea trial in the Xisha area of the South China Sea in August, 2021. The algorithm operates on the sensor data collected from the trial to obtain the vehicle’s kinematic model and accordingly design the parameter self-tuning controller. The performances demonstrate the accuracy of the model, and prove its generalization capability. With this model, the optimal controller has been designed, the control parameters have been self-tuned, and the response time and robustness of the system have been optimized,which validates the high efficiency on digital modelling for precision control of deep-sea mining vehicles.

Towards a Modeling of the Impacts of Road Verge Management on the Pollination Service Using System Dynamics: A Case Study in France

Several research studies have proven that eliciting and predicting the impact of human activity on ecosystem services will be crucial to support stakeholders’ awareness and to decide how to interact with the environment in a more sustainable manner. In this sense, the ecosystems known as road verges are particularly important because of their length and surface at an international scale, and their role in mitigating the damage done by roads. Plant pollination by insects is one of the most important ecosystem services. Because of its nature and the fact that they extend across a variety of landscapes, roadside can contribute to the maintenance of healthy ecosystems, under the condition of adapted management practices. This research is the first attempt to develop a System Dynamics-based aiming to estimate the ecological and economic impact of maintenance on the road verge pollination service in France. Maintenance strategies of road verges are simulated to compare their performance. The results show that there are ways to improve current maintenance strategies in terms of pollination value, but also that the model needs to consider other ecosystem services and synergistic effects that could further affect pollination to obtain more accurate estimations.

Neural Model-Based Self-Tuning PID Strategy Applied to PEMFC

This paper illustrates the benefits of a self-tuning PID strategy applied to a proton exchange membrane fuel cell system. Controller parameters are updated on-line, at each sampling time, based on an instantaneous linearization of an artificial neural network model of the process and a General Minimum Variance control law. The self-tuning PID scheme allows managing nonlinear behaviors of the system while avoiding heavy computations. The applicability, efficiency and robustness of the proposed control strategy are experimentally confirmed using varying control scenarios. In this aim, the original built-in controller is overridden and the self-tuning PID controller is implemented externally and executed on-line. Experimental results show good performance in setpoint tracking accuracy and robustness against plant/model mismatch. The proposed strategy appears to be a promising alternative to heavy computation nonlinear control strategies and not optimal linear control strategies.

Pavement performance model for road maintenance and repair planning: a review of predictive techniques

This paper provides a review of predictive analytics for roads,identifying gaps and limitations in current methodologies.It explores the implications of these limitations on accuracy and application,while also discussing how advanced predictive analytics can address these challenges.The article acknowledges the transformative shift brought about by technological advancements and increased computational capabilities.The degradation of pavement surfaces due to increased road users has resulted in safety and comfort issues.Researchers have conducted studies to assess pavement condition and predict future changes in pavement structure.Pavement Management Systems are crucial in developing prediction performance models that estimate pavement condition and degradation severity over time.Machine learning algorithms,artificial neural networks,and regression models have been used,with strengths and weaknesses.Researchers generally agree on their accuracy in estimating pavement condition considering factors like traffic,pavement age,and weather conditions.However,it is important to carefully select an appropriate prediction model to achieve a high-quality prediction performance system.Understanding the strengths and weaknesses of each model enables informed decisions for implementing prediction models that suit specific needs.The advancement of prediction models,coupled with innovative technologies,will contribute to improved pavement management and the overall safety and comfort of road users.

Optimized Binary Neural Networks for Road Anomaly Detection:A TinyML Approach on Edge Devices

Integrating Tiny Machine Learning(TinyML)with edge computing in remotely sensed images enhances the capabilities of road anomaly detection on a broader level.Constrained devices efficiently implement a Binary Neural Network(BNN)for road feature extraction,utilizing quantization and compression through a pruning strategy.The modifications resulted in a 28-fold decrease in memory usage and a 25%enhancement in inference speed while only experiencing a 2.5%decrease in accuracy.It showcases its superiority over conventional detection algorithms in different road image scenarios.Although constrained by computer resources and training datasets,our results indicate opportunities for future research,demonstrating that quantization and focused optimization can significantly improve machine learning models’accuracy and operational efficiency.ARM Cortex-M0 gives practical feasibility and substantial benefits while deploying our optimized BNN model on this low-power device:Advanced machine learning in edge computing.The analysis work delves into the educational significance of TinyML and its essential function in analyzing road networks using remote sensing,suggesting ways to improve smart city frameworks in road network assessment,traffic management,and autonomous vehicle navigation systems by emphasizing the importance of new technologies for maintaining and safeguarding road networks.

Resilience assessment and optimization method of city road network in the post-earthquake emergency period

The post-earthquake emergency period,which is a sensitive time segment just after an event,mainly focuses on saving life and restoring social order.To improve the seismic resilience of city road networks,a resilience evaluation method used in the post-earthquake emergency period is proposed.The road seismic damage index of a city road network can consider the influence of roads,bridges and buildings along the roads,etc.on road capacity after an earthquake.A function index for a city road network is developed,which reflects the connectivity,redundancy,traffic demand and traffic function of the network.An optimization model for improving the road repair order in the post-earthquake emergency period is also developed according to the resilience evaluation,to enable decision support for city emergency management and achieve the best seismic resilience of the city road network.The optimization model is applied to a city road network and the results illustrate the feasibility of the resilience evaluation and optimization method for a city road network in the post-earthquake emergency period.

Modeling continuous traffic flow with the average velocity effect of multiple vehicles ahead on gyroidal roads

In the future connected vehicle environment,the information of multiple vehicles ahead can be readily collected in real-time,such as the velocity or headway,which provides more opportunities for information exchange and cooperative control.Meanwhile,gyroidal roads are one of the fundamental road patterns prevalent in mountainous areas.To effectively control the system,it is therefore significant to explore the evolution mechanism of traffic flow on gyroidal roads under a connected vehicle environment.In this paper,we present a new continuum model with the average velocity of multiple vehicles ahead on gyroidal roads.The stability criterion and KdV-Burger equation are deduced via linear and nonlinear stability analysis,respectively.Solving the above KdV-Burger equation yields the density wave solution,which explores the formation and propagation property of traffic jams near the neutral stability curve.Simulation examples verify that the model can reproduce complex phenomena,such as shock waves and rarefaction waves.The analysis of the local cluster effect shows that the number of vehicles ahead and the radius information,and the slope information of gyroidal roads can exert a great influence on traffic jams.The effect of the first and second terms are positive,while the last term is negative.

基于ROAD的租车订购系统业务流程建模

基于业务架构为中心的企业架构开发思路,采用开放组架构框架(TOGAF)业务架构ACF元模型的划分,提出了一种ROAD架构迭代建模方法。针对目前ROAD元架构中业务活动模型采用IDEF0的活动模型导致的面向场景业务能力的不足,文中通过使用BPMN模型来覆盖IDEF0的活动建模,避免在描述复杂的业务过程时可能存在的局限性,并且更好地捕捉和表示所有的细节和关系,不会使得模型过于抽象。从而实现面向场景的系统建模和管理,提高业务架构设计的效率和精度。最后,文章中以旅游出行租车订购系统为例的方法,采用ROAD元架构方法建立场景化业务架构模型组,并对其进行讨论和改进,为现有架构体系提供一种面向场景的扩展思路。

Discourse Analysis of the Belt and Road Initiative From the Perspective of Central Asian Countries: A Case Study of the Republic of Kazakhstan

This study employs Norman Fairclough’s Critical Discourse Analysis(CDA)three-dimensional model,using the Republic of Kazakhstan as a case study,to delve into the discourse construction of China’s Belt and Road Initiative(BRI)in Central Asian countries.Through detailed analysis of policy documents,media reports,and public discussions in Central Asian countries,this paper reveals how the BRI constructs specific social practices,discourse events,and textual meanings within these nations.The findings indicate that through this global development strategy,China has not only strengthened its economic ties with Central Asian countries but has also exerted profound influences on political,cultural,and social levels.

24 Model在道路运输事故成因中的应用

为预防道路运输事故,基于事故致因“2-4”(24 Model)模型,对2015—2021年发生的215起道路运输事故的行为原因进行研究。从个人层面和组织层面分析导致事故发生的直接原因、间接原因、根本原因及其根源原因。结果表明:发生频次最多的不安全动作是驾驶员超速行驶(占10.67%);22.33%的道路运输事故存在管理者违章安排无资质员工上岗作业;管理者和领导者对57.67%的道路运输事故的发生有重要影响;由人为因素产生的不安全物态占比达71.53%;驾驶员、车辆安全生产管理制度欠缺或执行不到位在根本原因中尤为突出(占20.92%)。基于此,提出道路运输事故预防对策并构建出道路运输系统各因素关系图。

Grey-Markov Model for Road Accidents Forecasting

In order to improve the forecasting precision of road accidents, by introducing Markov chains forecasting method, a grey-Markov model for forecasting road accidents is established based on grey forecasting method. The model combines the advantages of both grey forecasting method and Markov chains forecasting method, overcomes the influence of random fluctuation data on forecasting precision and widens the application scope of the grey forecasting. An application example is conducted to evaluate the grey-Markov model, which shows that the precision of the grey-Markov model is better than that of grey model in forecasting road accidents.

Spatial-temporal Evolution and Determinants of the Belt and Road Initiative: A Maximum Entropy Gravity Model Approach

The spatial interaction model is an effective way to explore the geographical disparities inherent in the Belt and Road Initiative(BRI) by simulating spatial flows. The traditional gravity model implies the hypothesis of equilibrium points without any reference to when or how to achieve it. In this paper, a dynamic gravity model was established based on the Maximum Entropy(MaxEnt) theory to estimate and monitor the interconnection intensity and dynamic characters of bilateral relations. In order to detect the determinants of interconnection intensity, a Geodetector method was applied to identify and evaluate the determinants of spatial networks in five dimensions. The empirical study clearly demonstrates a heterogeneous and non-circular spatial structure. The main driving forces of spatial-temporal evolution are foreign direct investment, tourism and railway infrastructure construction, while determinants in different sub-regions show obvious spatial differentiation. Southeast Asian countries are typically multi-island area where aviation infrastructure plays a more important role. North and Central Asian countries regard oil as a pillar industry where power and port facilities have a greater impact on the interconnection. While Western Asian countries are mostly influenced by the railway infrastructure, Eastern European countries already have relatively robust infrastructure where tariff policies provide a greater impetus.

Future temperature changes over the critical Belt and Road region based on CMIP5 models

Based on data of 22 models from the Coupled Model Inter-comparison Project Phase 5(CMIP5),the performance of climate simulation is assessed and future changes under RCP2.6,RCP4.5 and RCP8.5 are projected over critical Belt and Road region.Compared with observations,the CMIP5 models simulate the linear trend and spatial distribution of the annual mean surface air temperature(SAT)better in the north(NBR)and south(SBR)of the Belt and Road region.The trend of the 22-model ensemble mean(CMIP5 MME)is 0.70/0.50
C per 100 years from 1901 to 2005,and the observed trend is 1.11/0.77
C per 100 years in the NBR/SBR region.After 1971,the relative error between CMIP5 MME and observations is 22%/15%in the NBR/SBR region.Seven/nine models are selected in the NBR/SBR to project future SAT changes under three RCP scenarios.For 2081e2100,warming in the NBR/SBR is projected to be(1.16±0.29)/(0.72±0.32)
C,(2.41±0.54)/(1.55±0.44)
C,and(5.23±1.02)/(3.33±0.65)
C for RCP2.6,RCP4.5,and RCP8.5,respectively.Under the RCP scenarios,the NBR region shows greater warming than the SBR region.The most significant warming is expected in Kazakhstan and the northern part of the SBR.The associated uncertainty generally increases with time under the three RCP scenarios.Furthermore,increases in warming over the Belt and Road region are more remarkable under higher-emission scenarios than lower-emission ones.

Complex Motion Modeling and State Estimation in Road Coordinates

Constrained modeling and state estimation have attracted much attention in recent years. This paper focuses on target motion modeling and tracking in road coordinates. An improved initialization method,which uses the optimal fusion of the position measurements in different directions,is presented for the constraint coordinate Kalman filter(CCKF). The CCKF is evaluated with a comprehensive comparison to the state-of-art linear equality constraint estimation methods. Numerical simulation results demonstrate the better performance of the CCKF. Then the interacting multiple model CCKF(IMM-CCKF) is proposed to manifest the advantages of the CCKF in complex motion modeling and state estimations. The effectiveness of the IMM-CCKF in maneuvering target tracking with spatial equality constraints is demonstrated by numerical experiments.

Stabilization of CSTR w ith Self-tuning Sliding Mode Controller Using T-S Fuzzy Linearization

A self-tuning reaching law based sliding mode control(SMC)theory is proposed to stabilize the nonlinear continuous stirred tank reactor(CSTR).T-S fuzzy logic is used to build a global fuzzy state-space linear model.Combing the traits of SMC and CSTR,three fuzzy rules can meet the requirements of controlled system.The self-tuning switch control law which can drive the state variables to the sliding surface as soon as possible is designed to ensure the robustness of uncertain fuzzy system.Lyapunov equation is applied to proving the stability of the sliding surface.The simulations show that the proposed approach can achieve desired performance with less chattering problem.

Research on the Model of General Landscape Planning of City Road System

This paper analyses the overall landscape framework of city road system in ancient and modern China, using the ideal state terrain model which was fi rst recorded by the construction system of the Zhou Dynasty, and summarizes the ideal model of overall landscape framework of city road system, based on Chinese psychological needs of landscape, and conducts the applied research taking Nanchong in Sichuan, China for example.

Evaluation and Forecasting of Wind Energy Investment Risk along the Belt and Road Based on a Novel Hybrid Intelligent Model

The timely and effective investment risk assessment and forecasting are of great significance to ensure the investment safety and sustainable development of wind energy along the Belt and Road.In order to obtain the scientific and real-time forecasting result,this paper constructs a novel hybrid intelligent model based on improved cloud model combined with GRA-TOPSIS and MBA-WLSSVM.Firstly,the factors influencing investment risk of wind energy along the Belt and Road are identified fromthree dimensions:endogenous risk,exogenous risk and process risk.Through the fuzzy threshold method,the final input index system is selected.Secondly,the risk evaluation method based on improved cloud model andGRA-TOPSIS is proposed.Thirdly,a modern intelligent model based on MBA-WLSSVMis designed.In modified bat algorithm(MBA),tent chaotic map is utilized to improve the basic bat algorithm,while weighted least squares support vector machine(WLSSVM)adopts wavelet kernel function to replace the traditional radial basis function to complete the model improvement.Finally,an example is given to verify the scientificity and accuracy of themodel,which is helpful for investors tomake fast and effective investment risk forecasting of wind energy along the Belt and Road.The example analysis proves that the proposedmodel can provide reference and basis for investment corpus to formulate the investment strategy in wind energy along the Belt and Road.