Landslide susceptibility mapping of mountain roads based on machine learning combined model

Landslide susceptibility mapping of mountain roads is frequently confronted by insufficient historical landslide sample data,multicollinearity of existing evaluation index factors,and inconsistency of evaluation factors due to regional environmental variations.Then,a single machine learning model can easily become overfitting,thus reducing the accuracy and robustness of the evaluation model.This paper proposes a combined machine-learning model to address the issues.The landslide susceptibility in mountain roads were mapped by using factor analysis to normalize and reduce the dimensionality of the initial condition factor and generating six new combination factors as evaluation indexes.The mountain roads in the Youxi County,Fujian Province,China were used for the landslide susceptibility mapping.Three most frequently used machine learning techniques,support vector machine(SVM),random forest(RF),and artificial neural network(ANN)models,were used to model the landslide susceptibility of the study area and validate the accuracy of this evaluation index system.The global minimum variance portfolio was utilized to construct a machine learning combined model.5-fold cross-validation,statistical indexes,and AUC(Area Under Curve)values were implemented to evaluate the predictive accuracy of the landslide susceptibility model.The mean AUC values for the SVM,RF,and ANN models in the training stage were 89.2%,88.5%,and 87.9%,respectively,and 78.0%,73.7%,and 76.7%,respectively,in the validating stage.In the training and validation stages,the mean AUC values of the combined model were 92.4% and 87.1%,respectively.The combined model provides greater prediction accuracy and model robustness than one single model.

The road map for green development of Western China

China has made some remarkable achievements in sustainable development,but the constant deterioration of the overall trend of the environment has not yet been effectively curbed.To achieve the goal of sustainable development,we must first ensure coordination and coherence of national development goals in different areas and adhere to green development road.To achieve the objectives related to green development,and to clarify the direction of green development in the next 20years,a road map is needed to guide and coordinate the process.This paper describes the concept of the green development road map,introduces a green development road map for the western region,and further elaborates it The road map clarifies the objectives and guiding principles of green development in the western region,points the areas that deserve more concern and institutional innovation,and builds a green development monitoring and evaluation(M&E)framework.Finally,the paper provides corresponding policy recommendations based on the established road map.

Study of the development road map of China’s renewable energy

Renewable energy (RE) has been attached high attention around the world due to its carbon-free and indigenous production in a sustainable way. China enjoys plenty of renewable energy resources, particularly the wind, solar, hydro- and biomass energy, which could be a sound basis for a large-scale exploitation. This report examines the current status of RE technology and industry, analyzes the challenges of promoting RE in China. In order to pave the way for a long-term development of RE, this paper outlines the basic principles and priorities for individual RE technology. In line with these, the paper puts forward the RE targets and further describes the RE road map by 2020, 2030 and extend to 2050, taking consideration of China’s RE resources, industrial basis and energy demand etc. At last, this paper provides some recommendations to ensure the achievements of the RE targets.

A Lane-Level Road Marking Map Using a Monocular Camera

The essential requirement for precise localization of a self-driving car is a lane-level map which includes road markings(RMs).Obviously,we can build the lane-level map by running a mobile mapping system(MMS)which is equipped with a high-end 3D LiDAR and a number of high-cost sensors.This approach,however,is highly expensive and ineffective since a single high-end MMS must visit every place for mapping.In this paper,a lane-level RM mapping system using a monocular camera is developed.The developed system can be considered as an alternative to expensive high-end MMS.The developed RM map includes the information of road lanes(RLs)and symbolic road markings(SRMs).First,to build a lane-level RM map,the RMs are segmented at pixel level through the deep learning network.The network is named RMNet.The segmented RMs are then gathered to build a lane-level RM map.Second,the lane-level map is improved through loop-closure detection and graph optimization.To train the RMNet and build a lane-level RM map,a new dataset named SeRM set is developed.The set is a large dataset for lane-level RM mapping and it includes a total of 25157 pixel-wise annotated images and 21000 position labeled images.Finally,the proposed lane-level map building method is applied to SeRM set and its validity is demonstrated through experimentation.

Efficient and reliable road modeling for digital maps based on cardinal spline

In order to realize an optimal balance between the efficiency and reliability requirements ofroad models,a road modeling method for digital maps based on cardinal spline is studied.First,the cardinal spline is chosen to establish an initial road model,which is specified by a series of control points and tension parameters.Then,in view of the initial road model,a gradual optimization algorithm,which can determine the reasonable control points and optimal tension parameters according to the degree of the change of road curvature,is proposed to determine the final road model.Finally,the proposed road modeling method is verified a d evaluated through experiments,and it is compared with the conventional method for digital maps based on the B-spline.The results show that the proposed method can resize a neaoptimal balance between the efficiency and reliability requirements.Compared with the conventional method based on the B-spline,this method occupies less data storage and achieves higher accuracy.

Development of a support system for creating disaster prevention maps focusing on road networks and hazardous elements

As a disaster prevention measure based on self-assistance and mutual assistance,disaster prevention maps are being created with citizen participation throughout Japan.The process of creating disaster prevention maps is itself a disaster prevention measure that contributes to raising awareness of disaster prevention by promoting exchange and cooperation within the region.By focusing on relations between road networks and hazardous elements,we developed a system to support disaster prevention map creation that visualizes roads at high risk during a disaster and facilitates the study of evacuation simulations.This system leads to a completed disaster prevention map in three phases.In the first phase,we use a device with GPS logging functions to collect information related to hazardous elements.In the second phase,we use Google Maps(“online map,”below)to visualize roads with high evacuation risk.In the final phase,we perform a regional evaluation through simulations of disaster-time evacuations.In experimental verifications,by conducting usability tests after creating a disaster prevention map in the target area,we evaluated the system in terms of simple operability and visibility.We found that by implementing this series of processes,even users lacking specialized knowledge regarding disaster prevention can intuitively discover evacuation routes while considering the relations between visualized road networks and hazardous elements.These results show that compared with disaster prevention maps having simple site notations using existing WebGIS systems,disaster prevention maps created by residents while inspecting the target area raise awareness of risks present in the immediate vicinity even in normal times and are an effective support system for prompt disaster prevention measures and evacuation drills.

Building of road boundary map based on laser radar

A method for road boundary detection and tracking using laser ladar with respect to a vehicle＇ s local coordinates is proposed. It can be applied to different types of road conditions, such as roads with or without curbs, having relatively rough road surface and with obstacles on road surface. In the method, some line segments are extracted after a series of preprocessing on range data. The extracted line segments are combined and further selected. They are then united to match the road models and generate the road boundary points which are tracked by Kalman filter. Then the obtained road boundary points are transformed to build a precise vector map by least squares fitting algorithm. These fitted line segments represent road boundary vectors. The vector map is precise enough to provide ample road information such as the orientation of road, the road width and the passable road region. Finally, extensive experiments conducted in urban and semi-urban environment demonstrate the robustness, effectiveness and viability of the proposed method.

Mapping Road Traffic Crashes in Sfax Governorate (Tunisia)

Sfax is one of the Tunisian governorates with a large number of road accidents, injuries and fatalities every year. This study aimed to analyze and map traffic accidents in this governorate. We analyzed the spatial distribution of accidents, their distribution by cause, by type of road, by size of traffic, by months of the year and days of the week. Accidents were correlated with several variables such as population numbers and densities, motorization rate, length and structure of the road network, and the amount of traffic. On the cartographic level, we have built a database, through which we have produced a series of thematic maps to argue this analysis. Through cartographic production, we also aimed to help road users, decision-makers and researchers in this area and in the field of transport. This work showed that Sfax occupies, among the other Tunisian governorates, an advanced position in gravity. Various human, climatic and technical factors explained this situation, of which human factors were the most important, and contributed to almost 90% of accidents. The current situation of accidents in Sfax requires a series of measures and actions to alleviate and mitigate the gravity of this phenomenon.

Summarizing vehicle driving decision-making methods on vulnerable road user collision avoidance

With the development of intelligent vehicles and autonomous driving technology,the safety of vulnerable road user(VRU)in traffic has been more guaranteed,and many research achievements have been made in the key area of collision avoidance decision-making methods.In this paper,the knowledge mapping method is used to mine the available literature in depth,and it is found that the research focus has shifted from the traditional accident cause analysis to emerging deep learning and virtual reality technology.This paper summarizes research on the three core dimensions of environmental perception,behavior cognition and collision avoidance decision-making in intelligent vehicle systems.In terms of perception,accurate identification of pedestrians and cyclists in complex environments is a major demand for VRU perception;in terms of behavior cognition,the coupling of VRU intention identification and motion trajectory prediction and other multiple factors needs further research;in terms of decision-making,the intention identification and trajectory prediction of collision objects are not included in the risk assessment model,and there is a lack of exploration specifically for cyclists'collision risk.On this basis,this paper provides guidance for the improvement of traffic safety of contemporary VRU under the conditions of intelligent and connected transportation.

数据驱动的学校周边道路拥堵识别

为定量分析多个中小学周边道路拥堵情况,针对性地改善学校周边拥堵状况,对比通学时期与非通学时期的交通拥堵数据.建立学校周边道路拥堵指标,通过拥堵评价函数,分析各道路受周边学校出行影响的程度,识别重点拥堵道路.提出了一种基于地图开放平台获取交通大数据的算法,对南京主城区79所中小学周边道路进行实例分析,给出了各道路受学校出行影响的拥堵值.结果表明:在通学期间交通整体拥堵程度高于非通学时期,通学时期平均拥堵指数为1.973,非通学时期平均拥堵指数为1.664,通学时期比非通学时期平均拥堵指数提高了18.57%.

数据中心主机房的能效评价方法及提升路径

本研究主要根据数据中心主机房的气流组织和UPS应用的实测结果,分析低负载率工况下气流组织存在的问题和UPS电源质量和运行效率,提出相应的机房能效提升路径,从而降低数据中心PUE值。

基于车载三维激光扫描的城市道路竣工测量探讨

车载三维激光扫描系统融合了多种传感器和数据源,可以自动、迅速地获取道路的全方位信息。其扫描速度迅捷、数据信息丰富、精确度高、采集过程安全简单,并能节省人力。此技术显著提高了外业生产效率,并降低了生产成本。对车载三维激光扫描技术在道路工程竣工测量中的内外业处理流程的研究结果表明:该技术的精度可达到1∶500测图精度要求,满足城市高架路竣工规划测绘的精度需求。该技术方案是切实可行的,且能高效地提高生产效率。

Optimal path finding algorithms based on SLSD road network model

A solution to compute the optimal path based on a single-line-single-directional（SLSD）road network model is proposed.Unlike the traditional road network model,in the SLSD conceptual model,being single-directional and single-line style,a road is no longer a linkage of road nodes but abstracted as a network node.Similarly,a road node is abstracted as the linkage of two ordered single-directional roads.This model can describe turn restrictions,circular roads,and other real scenarios usually described using a super-graph.Then a computing framework for optimal path finding（OPF）is presented.It is proved that classical Dijkstra and A algorithms can be directly used for OPF computing of any real-world road networks by transferring a super-graph to an SLSD network.Finally,using Singapore road network data,the proposed conceptual model and its corresponding optimal path finding algorithms are validated using a two-step optimal path finding algorithm with a pre-computing strategy based on the SLSD road network.

用MapInfo数据生成导航电子地图道路网络

提出一种为车辆导航系统生成具有拓扑结构道路网络的方法 ,并以北京市部分地图为例 。

智能网联汽车的高精地图数据交互模式

随着自动驾驶技术的发展,传统的地图服务已经不能满足以机器为主体的自动驾驶车辆的需要。具有更高精度、更多要素、更快更新频率的高精地图成为自动驾驶车辆的关键。不同来源数据之间的交互是高精地图用于自动驾驶导航决策的保证。针对智能网联汽车系统中高精地图的数据交互问题,本文首先对相关的研究进行了分析,依据高精地图的数据内容和数据来源对智能网联汽车中的高精地图数据进行再组织;然后详细阐述了基于车-路-云三方的高精地图数据交互的内容和交互机制,形成一个适用于智能网联汽车的高精地图数据交互模式;最后在仿真环境下对设计的数据交互模式进行了验证。

基于路网复杂度分区的轨迹分段地图匹配方法

针对现有大多数地图匹配方法在城市复杂环境下难以有效平衡匹配速度和精度的问题,提出了一种基于路网复杂度分区的轨迹分段地图匹配方法。该方法包括路网分区和轨迹分段匹配两个部分。通过构建的路网复杂度分区模型将路网划分为复杂区域和非复杂区域;对复杂区域内的轨迹段采用改进的隐马尔可夫模型进行匹配,非复杂区域内的轨迹段采用基于几何拓扑的快速匹配模型进行匹配;将不同区域内匹配的轨迹段进行拼接,得到完整轨迹的匹配结果。为得到路网复杂度分区模型的最优参数,进行了11组不同参数设置的对比实验,并将最终结果与ST-matching和传统隐马尔可夫模型两种地图匹配方法匹配的结果进行对比。结果表明,在三个数据集的匹配准确率均在96%以上,比其他两种对比算法匹配时间减少了60%,在保证匹配准确率的前提下有效提升了匹配效率。

MapInfo在CMMB路测分析中的应用研究

通过对中国移动多媒体广播(CMMB)路测系统和路测数据的简单分析,提出了一种CMMB无线网络分析的方法。在该方法中,首先将测试得到的大量CSV格式数据转化为Excel格式,然后通过MapInfo软件、平面地图和卫星地图制作分析数据用的电子地图,通过MapInfo软件分析路测的数据,在实际分析中进行了应用,最后给出了无锡某区域CMMB无线网络测试分析的实例,为该区域CMMB网络的优化提供了可靠依据。从应用中可以看到,该方法不仅节约网络测试分析的成本,还能很好的得到CMMB网络优化的关键参数。

基于改进HMM的车辆轨迹匹配方法研究

针对传统基于隐马尔可夫模型(HMM)的车辆轨迹匹配算法在复杂特殊路段无法兼备准确率与时间效率的问题,提出了基于改进HMM建模的车辆轨迹匹配方法。在候选路段的确定上通过生成网格索引提高整体检索效率,改进的HMM模型在发射概率的计算上综合航向角因素,考虑车辆速度对航向角造成的偏差影响,并设置经验因子进行调节。同时,考虑前后观测值误差过大及曲线路段等影响因素,采用单位采样间隔内的车辆实际行驶距离代替观测距离值,以保证传递概率计算的准确性。最后,利用实测数据进行试验,验证算法的性能。实验结果表明,所提方法匹配准确率约94.0%,相较于传统HMM轨迹匹配方法提高了2.8%,在提高时间效率及复杂路段的匹配准确度上也具有一定优势,单点匹配时间减少约0.9 ms,适用于交叉路口、立交桥、平行路段等复杂路况下的匹配。

生成式人工智能的教育应用监管路线图——UNESCO《教育和研究领域生成式人工智能使用指南》解读与启示

随着以ChatGPT为代表的生成式人工智能的问世及快速迭代,生成式人工智能对教育和研究产生巨大的促进作用,同时也带来前所未有的风险和挑战。2023年9月7日,联合国教科文组织发布首部关于生成式人工智能应用的全球指导性文件——《教育和研究领域生成式人工智能使用指南》。通过对该指南的文本分析,介绍文件的出台背景和内容梗概,讨论生成式人工智能所引发的争议和负面影响,呈现了教育和研究领域使用生成式人工智能的监管框架,绘制生成式人工智能在教育和研究领域的应用监管“路线图”,最后结合生成式人工智能在我国教育和研究中的使用现状,向政府部门、科技公司、学校等教育机构、教师、研究人员、学生分别提出相关建议:政府部门要完善政策框架,加强公众参与;科技公司要坚持以人为本,维护数字正义;学校等教育机构要优化校本政策,重塑学习环境,开展AI培训;教师要变革教学方式,开展人机合作,进行伦理示范;研究人员要拓展研究领域,提升研究效能;学生要合理使用工具,强化传统技能。

虚拟仿真资源面向“一带一路”推广应用研究——以测绘类虚拟仿真教学资源为例

本文梳理了国家建设虚拟仿真教学资源的政策脉络,分析了国内测绘类虚拟仿真教学资源的开发及现状,以期促进测绘类虚拟仿真资源在教育教学中的应用,并依托鲁班工坊建设,积极探索测绘类虚拟仿真教学资源面向“一带一路”沿线国家与地区的具体推广应用。