Road network comparison and matching techniques. a workflow proposal for the integration of traffic message channel and open source network datasets

The rapid growth of methods and techniques to acquire geospatial data has led to a wide availability of overlapping geographic datasets with different characteristics. Road network data sources are today a significant number, with high differences in level of detail and modelling schemas, depending on the main purpose. In addition, continuous information about people and freight movement is today available also in real-time. This type of data is today exchanged between traffic operators using referencing standards as Traffic Message Channel. Integrating these heterogeneous databases, in order to build an added value product, is a serious task in geographical data management. The paper is focus on techniques to conflate the Traffic message Channel logical network on Open Source road network dataset, in order to allow the precise visualisation of traffic data also in real-time. A first step of the research was the quality assessment of available Open Source (OS) road network dataset, then, a specific procedure to conflate data was set up, using an iterative process in order to reduce at every step the number of possible matching features. A first application of the enhanced OTM dataset is shown for the city of Turin: real-time open data of traffic flows recorded by road network fixed sensors, made available by the metropolitan Traffic Operation Centre (5T) and based on the TMC location referencing, are matched on the OTM road network, allowing a detailed real-time visualisation of traffic state

DEFINITION OF A METHODOLOGY TO DERIVE ROAD NETWORK FUNCTIONAL HIERARCHY CLASSES USING CAR TRACKING DATA

Road network functional hierarchy classifies individual roads into several levels, for efficient traffic management and road network generalization purposes. Automatic and semi-automatic road network extraction methods exist, but the generated products normally lack information on its functional hierarchy. This paper presents a methodology for automatically retrieve functional hierarchy for an OpenStreetMap derived road network from Floating Car Data, obtaining evenly distributed (e.g. for generalization purposes) or dynamic (e.g. to take into account differences in traffic volumes in different moments of the day) classifications. Road network elements are classified in function of vehicle speed values: the class distribution generated with the proposed methodology follows a linear distribution that can be better exploited for generalization purposes. Furthermore, the methodology allows to clearly distinguish different distributions in different moments of the day and days of the week, supporting traffic management activities

A SPATIAL DATABASE MODEL FOR MOBILITY MANAGEMENT

Abstract. In urban and metropolitan context, Traffic Operations Centres (TOCs) use technologies as Geographic Information Systems (GIS) and Intelligent Transport Systems (ITS) to tackling urban mobility issue. Usually in TOCs, various isolated systems are maintained in parallel (stored in different databases), and data comes from different sources: a challenge in transport management is to transfer disparate data into a unified data management system that preserves access to legacy data, allowing multi-thematic analysis. This need of integration between systems is important for a wise policy decision.This study aims to design a comprehensive and general spatial data model that could allow the integration and visualization of traffic components and measures. The activity is focused on the case study of 5T Agency in Turin, a TOC that manages traffic regulation, public transit fleets and information to users, in the metropolitan area of Turin and Piedmont Region.The idea is not to replace the existing implemented and efficient system, but to built-up on these systems a GIS that overpass the different software and DBMS platforms and that can demonstrate how a spatial and horizontal vision in tackling urban mobility issues may be useful for policy and strategies decisions. The modelling activity take reference from a review of transport standards and results in database general schema, which can be reused by other TOCs in their activities, helping the integration and coordination between different TOCs. The final output of the research is an ArcGIS geodatabase, which enable the customised representation of private traffic elements and measures.</p

Computational analysis of transport in three-dimensional heterogeneous materials: An OpenFOAM®-based simulation framework

Porous and heterogeneous materials are found in many applications from composites, membranes, chemical reactors, and other engineered materials to biological matter and natural subsurface structures. In this work we propose an integrated approach to generate, study and upscale transport equations in random and periodic porous structures. The geometry generation is based on random algorithms or ballistic deposition. In particular, a new algorithm is proposed to generate random packings of ellipsoids with random orientation and tunable porosity and connectivity. The porous structure is then meshed using locally refined Cartesian-based or unstructured strategies. Transport equations are thus solved in a finite-volume formulation with quasi-periodic boundary conditions to simplify the upscaling problem by solving simple closure problems consistent with the classical theory of homogenisation for linear advection–diffusion–reaction operators. Existing simulation codes are extended with novel developments and integrated to produce a fully open-source simulation pipeline. A showcase of a few interesting three-dimensional applications of these computational approaches is then presented. Firstly, convergence properties and the transport and dispersion properties of a periodic arrangement of spheres are studied. Then, heat transfer problems are considered in a pipe with layers of deposited particles of different heights, and in heterogeneous anisotropic materials

Floating car data (fcd) for mobility applications

Floating car data (FCD) is becoming more and more relevant for mobility domain applications, overcoming issues derived by the use of physical sensors (e.g. inductive loops, video observation, infrared and laser vehicle detection etc.), such as limited geographical distribution, measure inhomogeneities, limited or null coverage of minor roads. An increasing number of vehicles are equipped with devices capable of acquiring GPS positions and other data, transmitted in almost real-time to traffic control centres. Based on FCD data, several traffic analysis in support to mobility services can be performed: vehicle density, speed, origin-destination matrices, different patterns in function of vehicle type. If currently the representativeness of FCD can be considered an issue, current growing trend in FCD penetration should naturally overcome this issue. FCD are also higher sensitive to traffic events (e.g. traffic jams) than model-based approaches

Aerial LiDAR Technology in Support to Avalanches Prevention and Risk Mitigation: AN Operative Application at "colle della Maddalena" (italy)

Abstract. Snow avalanches are the result of unstable snow masses that detach from steep slopes as consequence of changes in snowpack structure. Nowadays, remote sensing technologies can improve the knowledge of avalanches phenomenon. This work focuses on the use of high point density aerial LiDAR (Light Detection And Ranging) technology as support to avalanche events prevention and risk mitigation, by presenting an operative application at Colle della Maddalena (Italy), along the road SS n. 21, nearby the French state border. The area is often involved in intense avalanche events that adversely impact on traffic and freight transport. For this reason, regional administrations will activate the Avalanche Artificial Detachment Intervention Plan (PIDAV, 2012) in order to prevent and manage the avalanche risk in the study area, also adopting artificial detachment systems. Main aim of the present work was to generate high resolution information related to geomorphological characterization (i.e. digital elevation models, slope and aspect) of avalanche sites derived from LiDAR data processing, that will help involved authorities in the management of the avalanche control plan. Digital elevation models at 0.5 m of spatial resolution were generated together with relative tridimensional models. Secondly, a preliminary investigation about capabilities and limits of LiDAR technology was done in the identification of avalanche sites only relying on geomorphological information directly derived by LiDAR data processing. Results showed that position of avalanche sites were correctly identified while no information could be obtained about the extension of the sliding area and identification of detachment areas

AN INTEGRATED APPROACH FOR POLLUTION MONITORING: SMART ACQUIREMENT AND SMART INFORMATION

Air quality is a factor of primary importance for the quality of life. The increase of the pollutants percentage in the air can cause serious problems to the human and environmental health. For this reason it is essential to monitor its values to prevent the consequences of an excessive concentration, to reduce the pollution production or to avoid the contact with major pollutant concentration through the available tools. Some recently developed tools for the monitoring and sharing of the data in an effective system permit to manage the information in a smart way, in order to improve the knowledge of the problem and, consequently, to take preventing measures in favour of the urban air quality and human health. In this paper, the authors describe an innovative solution that implements geomatics sensors (GNSS) and pollutant measurement sensors to develop a low cost sensor for the acquisition of pollutants dynamic data using a mobile platform based on bicycles. The acquired data can be analysed to evaluate the local distribution of pollutant density and shared through web platforms that use standard protocols for an effective smart use

Coherent Change Detection for repeated-pass interferometric SAR images: An application to earthquake damage assessment on buildings

During disaster response, the availability of relevant information, delivered in a proper format enabling its use among the different actors involved in response efforts, is key to lessen the impact of the disaster itself. Focusing on the contribution of geospatial information, meaningful advances have been achieved through the adoption of satellite earth observations within emergency management practices. Among these technologies, the Synthetic Aperture Radar (SAR) imaging has been extensively employed for large-scale applications such as flood areas delineation and terrain deformation analysis after earthquakes. However, the emerging availability of higher spatial and temporal resolution data has uncovered the potential contribution of SAR to applications at a finer scale. This paper proposes an approach to enable pixel-wise earthquake damage assessments based on Coherent Change Detection methods applied to a stack of repeated-pass interferometric SAR images. A preliminary performance assessment of the procedure is provided by processing Sentinel-1 data stack related to the 2016 central Italy earthquake for the towns of Amatrice and Accumoli. Damage assessment maps from photo-interpretation of high-resolution airborne imagery, produced in the framework of Copernicus EMS (Emergency Management Service - European Commission) and cross-checked with field survey, is used as ground truth for the performance assessment. Results show the ability of the proposed approach to automatically identify changes at an almost individual building level, thus enabling the possibility to empower traditional damage assessment procedures from optical imagery with the centimetric change detection sensitivity characterizing SAR. The possibility of disseminating outputs in a GIS-like format represents an asset for an effective and cross-cutting information sharing among decision makers and analysts