Location-related Information Research Articles

CoupledGT: Coupled Geospatial-temporal Data Modeling for Air Quality Prediction

Air pollution seriously affects public health, while effective air quality prediction remains a challenging problem since the complex spatial-temporal couplings exist in multi-area monitoring data of the city. Current approaches rarely consider relative geographical locations when capturing spatial-temporal relations, instead the latent inter-dependencies (i.e., implicit spatial relations) of data as a replacement. However, such relations cannot necessarily reflect the diffusion of air pollutants in the real world, and genuine location-related information could be lost during the implicit relation learning process. In this article, we introduce a new concept, geospatial-temporal data, and propose a novel deep neural network architecture, CoupledGT, to learn the geospatial-temporal couplings within data for air quality prediction. Specifically, the asymmetric diffusion relation of air quality data between two areas is first explicitly represented by the newly developed planar Gaussian diffusion (PGD) equation. And then, a geospatial couplings diffuser (GCD) is designed to parameterize the PGD equation and learn multi-areas diffusion mutually affected geospatial couplings. Besides, the RNN is employed to capture temporal couplings of each area, and incorporated with GCD to learn both shared and unique characteristics of the geospatial-temporal data simultaneously, which empowers the generalization and efficiency of the model. Extensive experiments on two real-world datasets demonstrate our method is robust and outperforms existing baseline methods in air quality prediction tasks.

Multiscale self-calibrated pulmonary nodule detection network fusing dual attention mechanism

Objective. In this study, we propose a model called DEPMSCNet (a multiscale self-calibration network) that has a high sensitivity and low false positive rate for detecting pulmonary nodules. Approach. First, at the feature extraction stage, we propose to use the REPSA-MSC module instead of the traditional convolutional neural network. The module extracts multiscale information from the feature map based on the image pyramid strategy while introducing adaptive convolutional branches to detect contextual information at each position of the multiscale, thereby expanding the receptive field and improving sensitivity. At the same time, multiple branches are adaptively weighted by channel attention, and the weights of different branches are adjusted to better generate pixel-level attention. Secondly, the proposed DSAM (dual-path spatial attention module) operates at the information fusion stage. This module fully exploits the rich spatial information of CT scans, obtains receptive field information from two branches, combines low-level feature map information with high-level semantic information, and enhances location-related information to effectively improve specificity. Thirdly, the focal loss function is used to solve the problem of positive and negative sample imbalance. Main results. The proposed model has been evaluated on the public lung nodule analysis (LUNA16) challenge dataset. The technique outperforms the most recent state-of-the-art detection algorithms in terms of sensitivity and specificity, obtaining a sensitivity of 0.988 and a competitive performance metric (CPM) of 0.963. Significance. Ablation experiments show that the two modules proposed in this paper effectively reduce false positives and improve sensitivity. This model effectively reduces the number of false positive nodules that doctors see on CT scans.

Divergence of tropical cyclone hazard based on wind-weighted track distributions in the Coral Sea, over 50 years

Tropical cyclones (TCs) with genesis in the Coral Sea present significant hazards to coastal regions in their surroundings. In addition, the erratic nature of TC tracks is not well understood in this region. Therefore, this study grouped Coral Sea TC tracks over the last fifty years based on K-means clustering of the maximum wind-weighted centroids. This was done in order to extract valuable new cyclone power, track curvature and location related information from their historical track records and to predict their behaviour in the light of a changing climate. TC track variance and curvature (sinuosity) were assessed. Three well-defined clusters of TC tracks were identified, and the results showed differing predominant directions of TC movement by cluster. Track sinuosity was shown to increase from east to west. Only one cluster showed a statistically significant trend (decreasing) in TC frequency. The TC power dissipation index (PDI) was used to reveal that two of the clusters have diverging trends for PDI post-2004. Based on the location of cyclone maximum intensity, only one cluster showed a statistically significant trend (towards the equator). All these findings demonstrated a clear variance in between-cluster hazard and show that TC trends discovered for the southwest Pacific are not manifest or consistent across all clusters.

УПРАВЛІННЯ НЕСТРУКТУРОВАНИМИ ДАНИМИ ПРИ ДОСЛІДЖЕННІ ВПЛИВУ ЗАСОБІВ МАСОВОЇ ІНФОРМАЦІЇ НА СОЦІАЛЬНУ БЕЗПЕКУ ЛЮДИНИ В УМОВАХ ГІБРИДНОЇ ВІЙНИ

Russia’s war against Ukraine significantly affects the state of Ukraine's national security and the social security of every Ukrainian. Its negative consequences are both direct and indirect in nature, which is a real social threat. The degree (level) of protection of the vital socio-economic interests of a person (person), his rights, freedoms and values from internal and external, real, and potential threats is an important element of assessing the state of the state's national security. In the conditions when Russia's military and informational aggression is directed against Ukraine, the study of issues of media influence, both from the inside and from the outside, on public opinion, moods among different categories of citizens, ethnic communities, their manifestations, and expression of will regarding political and economic transformations in the country is important and relevant. An important element of the analysis of the state of human social security in the conditions of a hybrid war is the study and assessment of the structure and dynamics of the development of public opinion in certain regions of the country. One of the most serious ways of assessing public opinion and forecasting its changes is, along with sociological research, monitoring the influence of mass media on the population of the region. Traditionally, such influence is assessed by monitoring local mass media and evaluating them according to a few formal qualities, such as audience coverage, ownership structure, political connections of owners, signs of commitment to one or another political position, etc. Such an analysis allows with a certain degree of accuracy to record the presence of various centers of influence on public opinion, however, it is completely insufficient to assess the real influence of these centers on public opinion itself, since it does not allow to assess exactly how this or that information influenced its consumers. When processing information flows from mass media, social networks, e-mail, etc., there are constant problems of processing large volumes of data containing textual information, which, although related to location, is not presented in traditional structured spatial formats, and does not explicitly contain geographic coordinates. Instead, its connection to geography is hidden in unstructured or semi-structured formats based on plain text. Unstructured data is usually considered to be information that does not have a predefined data structure or is not organized in a fixed order. Unstructured data, as a rule, is presented in the form of text, which can contain data such as dates, numbers, and facts, which immediately leads to difficulties in analysis, especially in the case of using traditional programs designed to work with structured data. Unstructured data is usually stored in human-readable formats, although such formats make automatic data management difficult. In connection with the rapid pace of progress and development of geoinformation systems, the specialists of the company Esri (USA) comprehensively approached the problem of the combined use of unstructured data and geoinformation systems. The Locate XT module was created to solve this problem. The new ArcGIS LocateXT add-on is designed to search, extract, and map data from plain text, including geographic coordinates in various formats, place names, and other location-related information. Geoinformation technology LocateXT is an effective tool for research and analysis of the impact of mass media in the conditions of a hybrid war between Russia and Ukraine on human social security, efficiency, completeness and quality of information and spatial support of management processes. It helps speed up decision-making procedures based on quality management of unstructured data obtained from disparate sources. Modern geoinformation tools in the decision-making support system provide an opportunity to systematize unstructured data, determine their geospatial reference, and subsequently conduct analysis and form the obtained results in a form convenient for perception.

Trajectory Recovery Based on Interval Forward–Backward Propagation Algorithm Fusing Multi-Source Information

In the tracking scheme in which global navigation satellite system (GNSS) measurement is temporally lost or the sampling frequency is insufficient, dead reckoning based on the inertial measurement unit (IMU) and other location-related information can be fused as a supplement for real-time trajectory recovery. The tracking scheme based on interval analysis outputs interval results containing the ground truth, which gives it the advantage of convenience in multi-source information fusion. In this paper, a trajectory-recovery algorithm based on interval analysis is proposed, which can conveniently fuse GNSS measurement, IMU data, and map constraints and then output an interval result containing the actual trajectory. In essence, the location-related information such as satellite measurement, inertial data, and map constraints is collected by practical experiments and then converted into interval form. Thereby, the interval-overlapping calculation is performed through forward and backward propagation to accomplish the trajectory recovery. The practical experimental results show that the trajectory recovery accuracy based on the proposed algorithm performs better than the traditional Kalman filter algorithm, and the estimated interval results deterministically contain the actual trajectory. More importantly, the proposed interval algorithm is approved to be convenient to fuse additional location-related information.

Spatial distribution and determinants of HIV prevalence among adults in urban Ethiopia: Findings from the Ethiopia Population-based HIV Impact Assessment Survey (2017–2018)

The design and evaluation of national HIV programs often rely on aggregated national data, which may obscure localized HIV epidemics. In Ethiopia, even though the national adult HIV prevalence has decreased, little information is available about local areas and subpopulations. To inform HIV prevention efforts for specific populations, we identified geographic locations and drivers of HIV transmission. We used data from adults aged 15-64 years who participated in the Ethiopian Population-based HIV Impact Assessment survey (October 2017-April 2018). Location-related information for the survey clusters was obtained from the 2007 Ethiopia population census. Spatial autocorrelation of HIV prevalence data were analyzed via a Global Moran's I test. Geographically weighted regression analysis was used to show the relationship of covariates. The finding indicated that uncircumcised men in certain hotspot towns and divorced or widowed individuals in hotspot woredas/towns might have contributed to the average increase in HIV prevalence in the hotspot areas. Hotspot analysis findings indicated that, localized, context-specific intervention efforts tailored to at-risk populations, such as divorced or widowed women or uncircumcised men, could decrease HIV transmission and prevalence in urban Ethiopia.

A novel privacy protection scheme for location-based services using collaborative caching

Protecting privacy in Location-based services (LBSs) has been one of the major concerns over the last few years. Users’ queries can reveal sensitive information to LBSs, thus giving an opportunity for the adversaries to track the querying user. Existing location privacy protection mechanisms either use a trusted third party (TTP) or a location obfuscation-based scheme. TTP-based schemes require users to report their location-related information to the TTP before sending a query to the location-based service provider (LSP). Once the TTP is compromised, users’ privacy can be breached. On the other hand, location obfuscation schemes achieve privacy at the cost of accuracy. We address these problems by proposing a collaborative caching-based privacy-preserving scheme for LBSs. The scheme prevents the users’ location information leakage from untrusted communication channels as well as LSP while maintaining the quality of service. The scheme uses a novel dummy location generation technique and a virtual identity mechanism to improve users’ location and identity privacy. Furthermore, one-time spatial groups are created with the nearby neighbors to access the localized services, thus providing less interaction with the untrusted LSP. The proposed work has the potential to preserve users’ location privacy and reduce the computation and communication costs of the system. The aforementioned claims are verified by the experimental results.

Autonomous Motivation and Self-Disclosure Intention

Studies that specifically discuss the formation of autonomous motivations of users of social networking services (SNSs) and how such motivation influences SNS user intention to disclose personal location-related information (PLRI) are absent from the literature. Consequently, this study, based on the self-determination theory and the information system success (ISS) model, investigates the relationships among key system-related quality factors, SNS users’ autonomous motivations and user satisfaction regarding an SNS, and their intentions to disclose PLRI. Survey data collected from 514 students at six universities were analyzed to validate our research model. Research results show that three system-related quality factors have different influences on user satisfaction and autonomous motivation, while both autonomous motivation and user satisfaction are significant antecedents of user intention to disclose PLRI. The research results have extended the application and advanced the understanding of ISS model and self-determination theory in the context of SNS.

Mobility-Aware Privacy-Preserving Mobile Crowdsourcing.

The increasing popularity of smartphones and location-based service (LBS) has brought us a new experience of mobile crowdsourcing marked by the characteristics of network-interconnection and information-sharing. However, these mobile crowdsourcing applications suffer from various inferential attacks based on mobile behavioral factors, such as location semantic, spatiotemporal correlation, etc. Unfortunately, most of the existing techniques protect the participant’s location-privacy according to actual trajectories. Once the protection fails, data leakage will directly threaten the participant’s location-related private information. It open the issue of participating in mobile crowdsourcing service without actual locations. In this paper, we propose a mobility-aware trajectory-prediction solution, TMarkov, for achieving privacy-preserving mobile crowdsourcing. Specifically, we introduce a time-partitioning concept into the Markov model to overcome its traditional limitations. A new transfer model is constructed to record the mobile user’s time-varying behavioral patterns. Then, an unbiased estimation is conducted according to Gibbs Sampling method, because of the data incompleteness. Finally, we have the TMarkov model which characterizes the participant’s dynamic mobile behaviors. With TMarkov in place, a mobility-aware spatiotemporal trajectory is predicted for the mobile user to participate in the crowdsourcing application. Extensive experiments with real-world dataset demonstrate that TMarkov well balances the trade-off between privacy preservation and data usability.

Analysis of Structural Steel Renewal Locations in Ship Repairing

Structural steel renewal in ship repairing is a routine work throughout the service/ operational life of a ship. Prior information about the renewal quantity helps the shipowners to allocate an appropriate budget and the shipyard to prepare the proper ship repairing schedule. It, in turn, helps the shipowners make a financial commitment for cargo. Likewise, prior information about the location of steel renewal works can help the shipowners to prepare the ship before going to the shipyard and the latter to plan for the required logistics. By doing so, the shipowners would be able to save cost in terms of less idle time in the shipyard. The latter can also increase the revenue in terms of minimizing mobilization time. Structural steel renewal location-related information for 123 cargo ships of various ages, deadweights, and types were collected from a single shipyard. Data of renewal locations of selected structural members were analyzed and presented in both tabular and graphical forms. The intention was to show the behavior of renewal locations with respect to the ship’s dimension appropriate for respective structural members, age, and length of the ship. In this article, the authors have attempted to identify and establish the possible root causes that influence the renewal locations and also to investigate and suggest the potential interrelationships between renewal locations, age, length, and type.

Sleeve insert scan body to predict implant placement position by using implant surgical guides: A dental technique

In studies that assessed the accuracy of implant surgical guides, evaluations were based on the placement position of the implant by using a manufactured surgical guide. However, such assessments could involve errors that may occur during implant placement. Therefore, evaluating the 3-dimensional accuracy of the fabrication of the implant surgical guide itself is not enough. In the evaluation method described in this article, location-related information is obtained by connecting a scan body to the sleeve of the surgical guide instead of directly placing the implant. This helps to evaluate the accuracy of the surgical guide without errors in the placement of an implant.

An overview of microblog user geolocation methods

Geographical locations of microblog users are essential for user profiling, event localization and target advertising, to name a few. Automatic identification of user locations from the corresponding microblogs has been explored for decades. This paper presents a survey of existing studies on the problem of microblog user geolocalisation. Specifically, we present a general framework for the geolocalisation of microblog users. In this framework, the location-related information is presented along with the overview of the microblogging platforms. Furthermore, the main steps of the framework are introduced in detail. The state-of-the-art geolocalisation methods are categorized into three classes: the text-based methods, the network-based methods, and the multiview-based methods. Moreover, the advantages and limitations of the existing methods are analyzed theoretically and experimentally. Survey findings conclude that multiview-based methods are superior to the text-based methods as well as the network-based methods. Besides that, existing user geolocalisation methods cannot capture the user's home location change, resulting in the misjudged results. Also, how to locate users from multiple social platforms is relatively unaddressed. Hence, the geolocalization of users across multiple social platforms is one of the problems that deserve further research.

위치정보 활용 측면을 고려한 공공데이터포털의 현황과 발전방안

Since the Korean open data portal service was launched in 2013, the level of data openness has expanded qualitatively and quantitatively, and numerous applications using open data portal have been developed. Since 70-80% of administrative data is location-related information, most of portal data also have spatial information. This study proposes spatial data improvement schemes of open data portal. To do this, at first, the 3 data types(file data, open API data, standard data) provided by open data portal was analyzed, and then status and problems of spatial data in portal were analyzed. Through status analysis, this paper suggests some improvements schemes. At first, a system for determining the data type, information level, and format of data should be established. Secondly, it is necessary to diversify data management institution and examine relevance of the data format. At last, in terms of the use of spatial information, the exact information of reference system should be provided and an integrated API for transforming the location reference system should be developed, and the handling process of irregular location reference system should be examined.

Study on multi-source data integrating standard and 3D cartographic visualization of urban flooding based on CityGML

Abstract. Urban flooding refers to the phenomenon of water accumulation in cities due to strong precipitation or continuous precipitation exceeding urban drainage capacity. At present, the problem of urban flooding has become another major urban disease after other urban problems such as crowded people, traffic congestion and environmental pollution (Weiwu W, et al. 2015). In order to better cope with the problem of waterlogging, there are many hydrodynamic models that can accurately simulate the process of pipe network drainage and water accumulation. However, there are currently two limitations that limit the practicality of these hydrodynamic models. First, these models often require a large amount of input data as a support. These data exhibit multi-source, heterogeneity, multi-scale, multi-resolution and other characteristics, which bring great difficulty to data acquisition and processing (Dongdong Z, et al. 2014). Second, the analysis results of the model contain a large amount of waterlogging related information. However, this information is usually represented by simple texts and tables, which is not conducive to interpretation, transmission and visualization. Especially for location-related information, such as flooding points, flow information of various parts of the drainage pipe, water depth in the flooded area, etc., non-interactive two-dimensional maps or tables can cause inconvenience to disaster management and decision-making.Therefore, a proper data model, respecting the needs of integration of multisource input and output data of waterlogging simulation and analysis and the needs of 3D visualization in front end, is needed. As a matter of fact, what in the last years has proven to be an emerging and effective approach is the adoption of standard-based, integrated semantic 3D virtual city models, which represent an information hub for most of the above-mentioned needs (Agugiaro et al. 2018). In particular, being based on open standards (e.g. on the CityGML standard proposed by the Open Geospatial Consortium), virtual city models firstly reduce the effort in terms of data preparation and provision. Secondly, they offer clear data structures, ontologies and semantics to facilitate data exchange between different domains and applications, and 3D visualization which is essential for crisis management (Herman L, Řezník T, 2015). However, a standardized and omni-comprehensive urban data model covering also the waterlogging domain is still missing. Even CityGML falls partially short when it comes to the definition of specific entities and attributes for waterlogging-related applications.This study aims to propose an Waterlogging Application Domain Extension (ADE) for CityGML 2.0, which is used to integrate the multi-source input data and the rich waterlogging related information of waterlogging simulation and analysis. In order to achieve the above objectives, the following contents will be investigated:(1) Analysis of urban hydrodynamic models’ function and their data characteristicsAnalyze the function of the hydrodynamic model in the city, and explore the source, precision and organizational structure of the input and output data during the simulation and analysis of waterlogging.(2) Construction of waterlogging data integration model based on CityGMLBased on the CityGML2.0 and its ADE mechanism, we will construct a conceptual model of waterlogging data integration standard, to describe the geometric and spatial structure of urban buildings, vegetation, land-use, underground pipelines, waterlogging related entities, etc. And more information about waterlogging, such as flooding points, drainage network carrying pressure, catchment area, etc., will be integrated into the corresponding fields of the data integration model in a reasonable way to facilitate Internet transmission and visualization.(3) Interactive 3D visualization method based on the proposed waterlogging ADEBased on the rich waterlogging-related information in the waterlogging ADE, combined with 3D visualization technology, the 3D dynamic interactive visualization method is explored, in order to provide more intuitive data support for waterlogging disaster management and decision-making.Through this study, we expect to improve the efficiency and practicability of the current waterlogging simulation and analysis by integrating the waterlogging related data from multi-source based on CityGML. As the result of the research, we intend to develop a prototype system based on the multi-source waterlogging related data integration method proposed in this study. With this standard, we can provide unified and standard data support for the waterlogging model. At the same time, the results of the waterlogging models are automatically integrated into the data set for dynamic 3D visualization.

Effectively Linking Persons on Cameras and Mobile Devices on Networks

Nowadays, ubiquitous surveillance cameras and wireless networks facilitate to understand online and offline human activities, and considerate attention has been paid to analyze people's activities by associating visual data with network data. It is critical to link mobile devices on networks to their owners in surveillance videos. However, existing works mainly rely on location-related information, while it is invalid to distinguish persons with similar movement behaviors due to the error-prone visual localization of person and wireless localization of devices. To solve this problem, in this paper we propose a framework called PD-Link that uses just one camera and one AP to infer links from mobile devices to their owners. To achieve its goal, PD-Link identifies a feature, person–device interactions (PDI), as the linking cue. We evaluate the performance of PD-Link in realistic scenarios and the experimental results demonstrate the efficacy and effectiveness of our method.

LoCO: Local Cooperative Data Offloading System Based on Location Information

The development of high speed mobile networks and the widespread use of smartphones have enabled users to easily obtain large data volumes via the Internet. This causes a heavy consumption of network resources, a burden on the available bandwidth. To solve such problems, a data offloading method with a wireless LAN access point has been used to distribute traffic from mobile to fixed networks. However, the method using wireless LAN access points can only change the communication paths but cannot reduce the overall traffic. This paper proposes a local cooperative data offloading system (LoCO) that reduces the overall traffic by sharing data, with direct communication between neighbors based on their location-related information. Moreover, the authors implemented the LoCO system on Android smartphones and clarified its performance in comparison with a traditional client/server system through experiments to download data in a real-world environment.

Multi-Attention Object Detection Model in Remote Sensing Images Based on Multi-Scale

Ground object detection, based on remote sensing satellite imagery, provides the groundwork of numerous applications, so the detection accuracy is of vital importance. The background of remote sensing images is complex, the object size is various, and there are many small objects. In view of the above problems, a multi-attention object detection method (MA-FPN) based on multi-scale is proposed in this paper, which can effectively make the network pay attention to the location of the object and reduce the loss of small object information. According to feature pyramid network (FPN), we firstly put forward a global spatial attention module, which extracts spatial location-related information from shallow features and fuses it with deep features to enhance the position expression ability of deep features. Besides, the paper provides a pixel feature attention module: the multi-scale convolution kernel is employed to generate the feature map of the same size as the input, as well as channel attention is used to assign weights to each layer of feature maps to obtain pixel-level attention maps with good details. Experiments on NWPU, RSOD, and DOTA datasets show that the proposed algorithm outperforms state of the art methods.

Residential Location and Psychological Distance in Americans' Risk Views and Behavioral Intentions Regarding Zika Virus.

Two 2017 experiments with a U.S. national opportunity sample tested effects of location, psychological distance (PD), and exposure to location-related information on Americans' Zika risk views and behavioral intentions. Location-distance from mosquito transmission of the virus in Florida and Texas; residence within states with 100+ Zika infections; residence within potential mosquito vector ranges-had small, inconsistent effects. Hazard proximity weakly enhanced personal risk judgments and concern about Zika transmission locally. It also increased psychological proximity, and intentions of mosquito control, avoiding travel to Zika-infected areas, and practicing safe sex. PD-particularly social and geographical distance, followed by temporal distance, with few effects for uncertainty-modestly and inconsistently decreased risk views and intentions. Exposure to location-related information from the U.S. Centers for Disease Control and Prevention website-naming states with 100+ Zika cases; maps of potential mosquito vector habitat-increased risk views and psychological closeness, but not intentions; maps had slightly stronger if inconsistent effects versus prevalence information. Structural equation modeling (SEM) of a location > PD > risk views > intention path explained modest variance in intentions. This varied in degree and kind (e.g., which location measures were significant) across behaviors, and between pre- and postinformation exposure analyses. These results suggest need for both theoretical and measurement advances regarding effects of location and PD on risk views and behavior. PD mediates location effects on risk views. Online background information, like that used here, will not enhance protective behavior without explicitly focused communication and perhaps higher objective risk.

Research on Constructing Demonstration Application in Electronic Map of Comprehensive Tourism Based on LBS

LBS (Location Based Service) is to determine the actual location by positioning technology or spatial database, and then provide users the location related service information that the users need. Comprehensive Tourism is the latest model of tourism development after the concept of Intelligent Tourism and a scientific system of tourism with resource optimization and orderly space which is panoramic and fully covered tourist industry. The construction of the platform of Comprehensive Tourism is beneficial to promote the development of regional tourism and improve the quality of tourism of tourist and promote the growth of regional economic. Building the system of the electronic map of Comprehensive Tourism in Yingtan as an example based on the analysis of the impact and role of LBS and electronic maps on tourism and tourism development patterns shows that the electronic map based on LBS has great impetus to the construction of regional tourism.

Implicit Cooperative Positioning in Vehicular Networks

Absolute positioning of vehicles is based on Global Navigation Satellite Systems (GNSSs) combined with on-board sensors and high-resolution maps. In cooperative intelligent transportation systems, the positioning performance can be augmented by means of vehicular networks that enable vehicles to share location-related information. This paper presents an implicit cooperative positioning (ICP) algorithm that exploits the Vehicle-to-Vehicle (V2V) connectivity in an innovative manner, avoiding the use of explicit V2V measurements such as ranging. In the ICP approach, vehicles jointly localize non-cooperative physical features (such as people, traffic lights, or inactive cars) in the surrounding areas, and use them as common noisy reference points to refine their location estimates. Information on sensed features are fused through V2V links by a consensus procedure, nested within a message passing algorithm, to enhance the vehicle localization accuracy. As positioning does not rely on explicit ranging information between vehicles, the proposed ICP method is amenable to implementation with off-the-shelf vehicular communication hardware. The localization algorithm is validated in different traffic scenarios, including a crossroad area with heterogeneous conditions in terms of feature density and V2V connectivity, and a real urban area by using Simulation of Urban MObility (SUMO) for traffic data generation. Performance results show that the proposed ICP method can significantly improve the vehicle location accuracy compared to the stand-alone GNSS, especially in harsh environments, such as in urban canyons, where the GNSS signal is highly degraded or denied.