数字地图在车辆导航中应用的若干技术问题

分析了对各种车辆导航系统对数字地图的使用需求，提出了车辆导航专用数字地图的特点和建立车辆导航专用数字地图库的必要性，在此基础上对数字地图在车辆导航中的若干技术问题作了一些探讨，并提出了一种在大范围内实现路径规划的技术途径。     

DEVELOPMENT OF A LOW-COST MOBILE MAPPING SYSTEM: A SOUTH AMERICAN EXPERIENCE

This paper presents the prototype of a low-cost terrestrial mobile mapping system (MMS) composed of a van, two digital video cameras, two GPS receivers, a notebook computer, and a sound frame synchronisation system. The imaging sensors are mounted as a stereo video camera on top of the vehicle together with the GPS antennae. The GPS receivers and the notebook computer are configured to record data referred to the vehicle position at a planned time interval. This position is subsequently transferred to the road images. This set of equipment and methods provide the opportunity to merge distinct techniques to make topographic maps and also to build georeferenced road image databases. Both vector maps and raster image databases, when integrated appropriately, can give spatial researchers and engineers a new technique whose application may realise better planning and analysis related to the road environment. The experimental results proved that the MMS developed at the São Paulo State University is an effective approach to inspecting road pavements, to map road marks and traffic signs, electric power poles, telephone booths, drain pipes, and many other applications important to people's safety and welfare. A small number of road images have already been captured by the prototype as a consequence of its application in distinct projects. An efficient organisation of those images and the prompt access to them justify the need for building a georeferenced image database. By expanding it, both at the hardware and software levels, it is possible for engineers to analyse the entire road environment on their office computers.     

利用反向流和冲突消除进行人车混行疏散路网优化

城市突发事件时人员会选择不同的交通方式（步行、驾车等）进行疏散,在交叉口处极易出现人车混行。提出了一种利用反向流和冲突消除策略的人车混行疏散路网优化方法,对主要疏散路径进行人、车流的反向以扩大通行能力,并在交叉口处禁止人、车的部分转向来进行人车分流以消减人车冲突。建立了以平均疏散时间和平均疏散路径长度最小为目标的优化模型,并利用遗传算法求解最优的人车分流方案。以武汉市2 km范围内的路网为例进行实验,并针对不同的人车混合比例进行模型敏感性分析。结果表明,所提方法能通过人车混行网络设计提高疏散效率,在行人比例较高的情况下,平均疏散时间及疏散路径长度的改善较明显。     

应用光照模型的交通车辆识别定位的高分辨率遥感方法

针对识别和定位路面上每辆汽车这一交通遥感图像处理的核心环节,提出一种解决方法。应用光照模型推导出路面、车辆(深浅两类)、汽车阴影在全色遥感影像中的亮度差异与亮度变化特征。以亮度差异为基础建立了能够将车辆区域从路面遥感图像中分割出来的图层分离算法。针对密集行驶的汽车因阴影相互覆盖而容易被误识别为一辆大型车的问题以及浅色车因深色车窗造成的识别结果割裂问题,利用亮度变化特征以及阴影、车窗与汽车的位置关系设计了车辆区域内的阴影和车窗干扰消除算法,通过闭运算实现了遥感图像中的汽车识别与定位。选用10幅交通遥感图像进行了测试,对浅色车的识别率大于92%,对深色车的识别率大于87%。     

Spatial Prediction of Landslide Hazard Using Logistic Regression and ROC Analysis

An empirical modeling of road related and non‐road related landslide hazard for a large geographical area using logistic regression in tandem with signal detection theory is presented. This modeling was developed using geographic information system (GIS) and remote sensing data, and was implemented on the Clearwater National Forest in central Idaho. The approach is based on explicit and quantitative environmental correlations between observed landslide occurrences, climate, parent material, and environmental attributes while the receiver operating characteristic (ROC) curves are used as a measure of performance of a predictive rule. The modeling results suggest that development of two independent models for road related and non‐road related landslide hazard was necessary because spatial prediction and predictor variables were different for these models. The probabilistic models of landslide potential may be used as a decision support tool in forest planning involving the maintenance, obliteration or development of new forest roads in steep mountainous terrain.     

GIS applications for military operations in coastal zones

In order to successfully support current and future US military operations in coastal zones, geospatial information must be rapidly integrated and analyzed to meet ongoing force structure evolution and new mission directives. Coastal zones in a military-operational environment are complex regions that include sea, land and air features that demand high-volume databases of extreme detail within relatively narrow geographic corridors. Static products in the form of analog maps at varying scales traditionally have been used by military commanders and their operational planners. The rapidly changing battlefield of 21st Century warfare, however, demands dynamic mapping solutions. Commercial geographic information system (GIS) software for military-specific applications is now being developed and employed with digital databases to provide customized digital maps of variable scale, content and symbolization tailored to unique demands of military units. Research conducted by the Center for Remote Sensing and Mapping Science at the University of Georgia demonstrated the utility of GIS-based analysis and digital map creation when developing large-scale (1:10,000) products from littoral warfare databases. The methodology employed–selection of data sources (including high resolution commercial images and Lidar), establishment of analysis/modeling parameters, conduct of vehicle mobility analysis, development of models and generation of products (such as a continuous sea–land DEM and geo-visualization of changing shorelines with tidal levels)–is discussed. Based on observations and identified needs from the National Geospatial-Intelligence Agency, formerly the National Imagery and Mapping Agency, and the Department of Defense, prototype GIS models for military operations in sea, land and air environments were created from multiple data sets of a study area at US Marine Corps Base Camp Lejeune, North Carolina. Results of these models, along with methodologies for developing large-scale littoral warfare databases, aid the National Geospatial-Intelligence Agency in meeting littoral warfare analysis, modeling and map generation requirements for US military organizations.     

Obstacle‐limited service coverage: A new geospatial approach

Service coverage is a crucial component of spatial decision‐making involving facility siting. Determining coverage requires knowledge about travel behavior and accessibility across a region, as well as supporting methods of measurement and derivation when actual travel cannot necessarily be observed. When service is provided using vehicles that move freely without the restriction posed by roads (e.g., airplanes, helicopters, unmanned aerial vehicles/drones, boats, ships, and submarines), straight‐line travel is regularly assumed. However, this common assumption is often violated by geographical obstructions, resulting in overestimated service coverage. To date, no methods have been developed capable of accounting for obstacle‐limited service coverage. Existing methods either completely ignore obstructions or crudely estimate impacts on travel. In this research, we develop a novel method, ESP‐Cover, capable of deriving accurate obstacle‐limited coverage without introducing representation errors. A location siting problem involving drone‐based package delivery is presented to highlight the necessity of explicitly accounting for obstacles in system design. The assessment results demonstrate the importance of accurate derivation of the obstacle‐limited coverage for spatial decision‐making and the accuracy of the ESP‐Cover method in coverage derivation.     

视频卫星数据的运动车辆提取

针对新兴的视频卫星数据在道路监控中应用研究较少的问题,该文提出了一种基于特征的视频卫星数据运动车辆提取优化算法。以SkySat-1卫星获取的Las Vegas地区的视频数据为实验数据,采用传统帧差法与阈值法进行运动车辆提取,验证了该方法对视频卫星数据处理的有效性,同时在此基础上根据空间特征信息界定出道路以内的感兴趣区域,进一步根据车辆的几何特征信息对感兴趣区域的疑似车辆目标进行筛选。实验结果表明:所提算法在提取精度和完整度上明显优于基于像素的帧差法与阈值法的提取结果。     

Automatic mapping and innovative on-demand mapping services at IGN France

Mapmaking has become widespread through the Internet, resulting in a wide range of cartographic quality. To achieve better quality, mapmaking needs tools and online services for intuitive and efficient on-demand mapping. A project team at IGN, the French National Mapping Agency, is working on producing a digital cartographic model (DCM) from various existing databases and maps on which such tools and services are based. This DCM ranges from detailed topographic maps to small general road maps. GeoServer Web Map Service capabilities were used extensively to produce quality maps with various legends. Special care was taken to make a default legend suitable for customer data overlays, both on-screen and on paper. Web-based interface prototypes were built to guide users in choosing colors and creating their own original map legends. Users can also rely on a growing catalog of harmonious color palettes and map samples as sources of inspiration.     

Spatial Prediction of Landslide Hazard Using Fuzzy k-means and Dempster-Shafer Theory

Landslide databases and input parameters used for modeling landslide hazard often contain imprecisions and uncertainties inherent in the decision‐making process. Dealing with imprecision and uncertainty requires techniques that go beyond classical logic. In this paper, methods of fuzzy k‐means classification were used to assign digital terrain attributes to continuous landform classes whereas the Dempster‐Shafer theory of evidence was used to represent and manage imprecise information and to deal with uncertainties. The paper introduces the integration of the fuzzy k‐means classification method and the Dempster‐Shafer theory of evidence to model landslide hazard in roaded and roadless areas illustrated through a case study in the Clearwater National Forest in central Idaho, USA. Sample probabilistic maps of landslide hazard potential and uncertainties are presented. The probabilistic maps are intended to help decision‐making in effective forest management and planning.     

Real World Map Reading Strategies

Abstract

A main component of an Intelligent Transport System (ITS) is the navigation system to be used in the 'intelligent' vehicle. In fact, driving a vehicle is a complex task as the driver has to control the vehicle as well as make decisions about getting to his/her destination. Too much information on the map (i.e. a complex map) would confuse the driver, while too little information would not be sufficient for the user to compare with the actual environment. Sufficient information but poorly presented would also result in confusion.

This paper reports on a study of the design of dynamic maps for land vehicle navigation through an investigation of different designs with different levels of information contents and visual effects using various dynamic variables. Different colours were assigned to the road and building names according to the position of the vehicle, and blinking symbols were used to indicate direction restrictions. Also, a set of maps with different degrees of complexity was designed such that the users would be provided with different levels of contents and representation by zooming in and out. The effectiveness of the design was investigated in the map evaluation process.     

Finding outdoor boundaries for 3D space‐based navigation

Outdoor navigation is widely used in daily life, but faces various issues related to the fidelity of outdoor navigation networks. For instance, agents (pedestrians) are often guided via unrealistic detours around places without clear paths (e.g., squares) or if there are vertical constraints such as overpasses/bridges. This is partly explained by the fact that the main sources of navigation networks in current outdoor navigation are two‐dimensional road/street networks. Utilizing a three‐dimensional space‐based navigation model, compatible with some indoor approaches, is a popular way to address the above‐mentioned issues. A 3D space‐based navigation model is generated by treating 3D spaces as nodes and the shared faces as edges. Inputs of this model are enclosed 3D spaces (volumes). However, outdoor spaces are generally open and unbounded. This article puts forward an approach to enclose outdoor spaces and mimic the indoor environments to derive a network based on connectivity and accessibility of spaces. The approach uses 2.5D maps and consists of three major steps: object footprint determination, footprint classification and space creation. Two use cases demonstrate the proposed approach. Enclosing outdoor spaces opens a new research direction toward providing seamless indoor/outdoor navigation for a range of agents.     

High Resolution Köppen‐Geiger Classifications of Paleoclimate Simulations

The development and application of an algorithm to compute Köppen‐Geiger climate classifications from the Coupled Model Intercomparison Project (CMIP) and Paleo Model Intercomparison Project (PMIP) climate model simulation data is described in this study. The classification algorithm was applied to data from the PMIP III paleoclimate experiments for the Last Glacial Maximum, 21k years before present (yBP), Mid‐Holocene (6k yBP) and the Pre‐Industrial (0k yBP, control run) time slices. To infer detailed classification maps, the simulation datasets were interpolated to a higher resolution. The classification method presented is based on the application of Open Source Software, and the implementation is described with attention to detail. The source code and the exact input data sets as well as the resulting data sets are provided to enable the application of the presented approach.     

A Technology Fusion Approach for Augmented Reality Applications

Technology fusion through the integration of multiple sensors and data sources is a common feature of current spatial information applications. Augmented reality is one such example. Augmented reality is a technology that overlays views of the real world with digital information. It provides an innovative application of spatial databases and can be applied in both real‐time and non‐real‐time applications. In order to correctly align digital (augmented) data with views of the real world, position and attitude determination is required. This paper describes the development of an augmented reality prototype, which includes the design and implementation of a position and attitude determination component. The prototype relies on spatial information to improve the accuracy of position and attitude determination. Successful field tests of the prototype are carried out in Melbourne, Australia. The field tests are focused around a case study that uses the prototype to enable drivers to ‘see’ the road and surrounding vehicles despite heavy fog, rain, snow or other low visibility situations.     

Modeling the traffic disruption caused by pluvial flash flood on intra‐urban road network

Pluvial flash flood (PFF) can cause serious traffic disruption in big cities. We conducted interdisciplinary research by integrating flood modeling and traffic analysis to reveal the spatiotemporal pattern of the interplay between these two processes. A simplified simulation tool, which is capable of building a road network model, assigning trip paths with the effect of road closures, and evaluating travel delay and vehicle volume redistribution in a given PFF scenario, was developed to capture the traffic disruption in the face of PFF events. Modeling outputs from a case study in the city center of Shanghai showed that the delay of vehicles diverted to dry links or trapped in flooded links may reach 0.5 to 8 times the travel time in no‐flood scenarios. Overall, approximately 1–7% of vehicle volumes on flooded links would be redistributed onto dry links (more likely major arterial roads). However, the vehicle volume variation during each time interval demonstrated evident disparity with the spatiotemporal change of flood inundations. Simulating and mapping the congestion can largely facilitate the identification of vulnerable links. Future research will test the method in other intra‐urban areas and try to bridge the gap between modeling outputs and smart city planning and management.     

A general map matching algorithm for transport telematics applications

This paper describes a map-matching algorithm designed to support the navigational functions of a real-time vehicle performance and emissions monitoring system currently under development, and other transport telematics applications. The algorithm is used together with the outputs of an extended Kalman filter formulation for the integration of GPS and dead reckoning data, and a spatial digital database of the road network, to provide continuous, accurate and reliable vehicle location on a given road segment. This is irrespective of the constraints of the operational environment, thus alleviating outage and accuracy problems associated with the use of stand-alone location sensors. The map-matching algorithm has been tested using real field data and has been found to be superior to existing algorithms, particularly in how it performs at road intersections.     

Integrated compression of vehicle spatio‐temporal trajectories under the road stroke network constraint

With fast growth of all kinds of trajectory datasets, how to effectively manage the trajectory data of moving objects has received a lot of attention. This study proposes a spatio‐temporal data integrated compression method of vehicle trajectories based on stroke paths coding compression under the road stroke network constraint. The road stroke network is first constructed according to the principle of continuous coherence in Gestalt psychology, and then two types of Huffman tree—a road strokes Huffman tree and a stroke paths Huffman tree—are built, based respectively on the importance function of road strokes and vehicle visiting frequency of stroke paths. After the vehicle trajectories are map matched to the spatial paths in the road network, the Huffman codes of the road strokes and stroke paths are used to compress the trajectory spatial paths. An opening window algorithm is used to simplify the trajectory temporal data depicted on a time–distance polyline by setting the maximum allowable speed difference as the threshold. Through analysis of the relative spatio‐temporal relationship between the preceding and latter feature tracking points, the spatio‐temporal data of the feature tracking points are all converted to binary codes together, accordingly achieving integrated compression of trajectory spatio‐temporal data. A series of comparative experiments between the proposed method and representative state‐of‐the‐art methods are carried out on a real massive taxi trajectory dataset from five aspects, and the experimental results indicate that our method has the highest compression ratio. Meanwhile, this method also has favorable performance in other aspects: compression and decompression time overhead, storage space overhead, and historical dataset training time overhead.     

A Service Oriented Geoprocessing System for Real‐Time Road Traffic Monitoring

The emergence of the Sensor Web has paved the way for a new set of innovative software applications that exploit the enhanced availability of real‐time information. This article describes one such application built from Sensor Web components that aggregates GPS track data from a fleet of vehicles to provide an overview of road traffic congestion at the city scale. The application embodies a Service Oriented Architecture; web service components are used to archive and pre‐process incoming sensor observations, to encapsulate a horizontally partitioned spatial database that performs geoprocessing, and to disseminate results to client applications. Our results confirm that floating car data can provide an accurate depiction of current road traffic conditions. The presented solution uses Open Geospatial Consortium web services where possible and serves to highlight the difficulties inherent in achieving horizontal database scalability in sensor based geoprocessing systems.     

Mining Trajectory Data and Geotagged Data in Social Media for Road Map Inference

As mapping is costly and labor‐intensive work, government mapping agencies are less and less willing to absorb these costs. In order to reduce the updating cycle and cost, researchers have started to use user generated content (UGC) for updating road maps; however, the existing methods either rely heavily on manual labor or cannot extract enough information for road maps. In view of the above problems, this article proposes a UGC‐based automatic road map inference method. In this method, data mining techniques and natural language processing tools are applied to trajectory data and geotagged data in social media to extract not only spatial information – the location of the road network – but also attribute information – road class and road name – in an effort to create a complete road map. A case study using floating car data, collected by the National Commercial Vehicle Monitoring Platform of China, and geotagged text data from Flickr and Google Maps/Earth, validates the effectiveness of this method in inferring road maps.     

Automatic Update of Road Attributes by Mining GPS Tracks

Despite advances in cartography, mapping is still a costly process which involves a substantial amount of manual work. This article presents a method for automatically deriving road attributes by analyzing and mining movement trajectories (e.g. GPS tracks). We have investigated the automatic extraction of eight road attributes: directionality, speed limit, number of lanes, access, average speed, congestion, importance, and geometric offset; and we have developed a supervised classification method (decision tree) to infer them. The extraction of most of these attributes has not been investigated previously. We have implemented our method in a software prototype and we automatically update the OpenStreetMap (OSM) dataset of the Netherlands, increasing its level of completeness. The validation of the classification shows variable levels of accuracy, e.g. whether a road is a one‐ or a two‐way road is classified with an accuracy of 99%, and the accuracy for the speed limit is 69%. When taking into account speed limits that are one step away (e.g. 60 km/h instead of the classified 50 km/h) the classification increases to 95%, which might be acceptable in some use‐cases. We mitigate this with a hierarchical code list of attributes.