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.

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.

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.

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.

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.

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 。

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。

电子地图组件TeleMap的设计与实现

阐述了TeleMap电子地图组件的整体构架及组件构成,并在VB平台下实现了一个电子地图系统,验证了TeleMap组件的可用性。

利用Google Maps API提取交通地理数据的可行性研究

Google Maps的开放策略为WebGIS的应用开发提供方便的平台。针对具有特殊需求的路径规划、行车诱导等应用,在深入分析路网组成及寻径特点的基础上,探讨如何根据Google Maps API提取局部交通地理数据的策略,并提出以探索法进行提取的算法思路。

Google Maps中的局部交通道路数据提取方法研究

Google Maps API的开放策略将基于WebGIS的应用开发推向了一个新的高潮。针对具有特殊需求的路径规划、行车诱导等应用,本文在深入分析路网特点及寻径方式的基础上,探讨了如何根据Google Maps API提供的对象和方法提取局部交通道路数据的策略,并针对一些特殊道路类型设计了提取算法。

A novel vehicle navigation map matching algorithm based on fuzzy logic and its application

A new real-time map matching algorithm based on fuzzy logic is proposed. 3 main factors affecting the reliability of map matching, including the distance between the vehicle location and the matching road segment, the angle between the vehicle direction and the road segment direction and the road connectivity are discussed. Fuzzy rules for the distance, angle and connectivity are presented to calculate the matching reliability. 2 indicators for estimating the matching reliability are then derived, one is the lower limit of the reliability, and the other is the limit error of the difference between the maximal value and the second-maximal value of the reliability. A real-time map-matching system based on fuzzy logic is therefore developed. Using the real data of global positioning system(GIS) based navigation and geographic information system(GPS) based road map, the method is verified and the (results) prove the effectiveness of the proposed method.

A Spatio-temporal Data Model for Road Network in Data Center Based on Incremental Updating in Vehicle Navigation System

The technique of incremental updating,which can better guarantee the real-time situation of navigational map,is the developing orientation of navigational road network updating.The data center of vehicle navigation system is in charge of storing incremental data,and the spatio-temporal data model for storing incremental data does affect the efficiency of the response of the data center to the requirements of incremental data from the vehicle terminal.According to the analysis on the shortcomings of several typical spatio-temporal data models used in the data center and based on the base map with overlay model,the reverse map with overlay model (RMOM) was put forward for the data center to make rapid response to incremental data request.RMOM supports the data center to store not only the current complete road network data,but also the overlays of incremental data from the time when each road network changed to the current moment.Moreover,the storage mechanism and index structure of the incremental data were designed,and the implementation algorithm of RMOM was developed.Taking navigational road network in Guangzhou City as an example,the simulation test was conducted to validate the efficiency of RMOM.Results show that the navigation database in the data center can response to the requirements of incremental data by only one query with RMOM,and costs less time.Compared with the base map with overlay model,the data center does not need to temporarily overlay incremental data with RMOM,so time-consuming of response is significantly reduced.RMOM greatly improves the efficiency of response and provides strong support for the real-time situation of navigational road network.

Traffic Maps and Smartphone Trajectories to Model Air Pollution, Exposure and Health Impact

In this study, we explored to combine traffic maps and smartphone trajectories to model traffic air pollution, exposure and health impact. The approach was step-by-step modeling through the causal chain: engine emission, traffic density versus traffic velocity, traffic pollution concentration, exposure along individual trajectories, and health risk. A generic street with 100 km/h speed limit was used as an example to test the model. A single fixed-time trajectory had maximum exposure at velocity of 45 km/h at maximum pollution concentration. The street population had maximum exposure shifted to a velocity of 15 km/h due to the congestion density of vehicles. The shift is a universal effect of exposure. In this approach, nearly every modeling step of traffic pollution depended on traffic velocity. A traffic map is a super-efficient pre-processor for calculating real-time traffic pollution exposure at global scale using big data analytics.

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.

从有路导航走向无路导航

在有路导航日臻成熟的背景下,该文结合车辆在复杂非结构化环境下对导航服务的需求,提出了无路导航这一多学科交叉研究方向。首先,介绍了有路导航的发展及无路导航的需求场景,基于车辆通过性的4个具体方面,提出了从有路导航走向无路导航过程中的科学问题,包括地理-地质通行要素精细化遥感探测、无路区域软地面土壤参数遥感获取、天候变化对地面特性的定量影响机理,阐明了无路导航的研究导向;然后,归纳了车辆通过性计算与表征技术、无路区域数字路网构建技术、无路导航智能路径规划技术等关键技术,提出了“无路区域有路化”的技术途径与无路区域数字路网的概念,构建了无路导航技术体系;最后,结合无路导航相关技术的实践,展现了其应用潜力。无路导航的研究将进一步丰富导航的内涵,拓宽导航应用边界。

遥感影像路径规划中A*算法优化研究

针对在高分辨率遥感影像上进行路径规划时所面临的算法搜索范围大,效率低且转折点较多等问题,提出一种基于A*算法的全局路径规划算法。在原始A*算法的启发函数部分引入余弦函数,减少冗余节点的搜索过程,缩小算法搜索节点的范围,提升算法运行效率;设计拐点优化方案,减少规划路径中不必要的拐点数,提升路径规划结果的平滑性。为验证改进方法的有效性,在Matlab软件中进行仿真实验,分析原始A*算法和改进后A*算法的搜索节点范围与路径中拐点数量,并在遥感影像的二值地图中进行真实路径规划对比实验,分析路径长度与运行时间。实验数据表明,改进后算法的扩展节点减少30%以上,非必要拐点数减少35%以上,路径规划长度缩短10.1%,运行时间减少10.7%,提升了寻求最优路径的效率。