IPS Vial R&D&I Projects

CAMARIA Project

CAMARIA develops a cost-effective predictive maintenance tool, based on 3D cameras mounted on road surveillance vehicles, and AI modules to detect deficiencies in the road surface and identify road signs.

Learn from visual inputs to estimate future road degradation and create an up-to-date inventory of each type of traffic sign using a hierarchical approach.

e-Miles electric car

e-Miles is presented as an innovative, inclusive, and sustainable mobility solution. It is an expandable quadricycle, accessed from the front to facilitate entry for people with reduced mobility, and whose parts are manufactured using 3D printing. IPS Vial is collaborating with The e-Miles Company on the implementation of the e-Miles electric car prototype in various urban areas.

InCa RECON Project

The InCa Recon project has been co-financed by the European Regional Development Fund (ERDF) with the aim of promoting technological development, innovation, and high-quality research. File number: IDI-20190219
The InCa Recon project's overall objective is to create an autonomous tool for identifying traffic signs and informational signs for efficient inventory and maintenance applications, as well as new value-added services. The main challenge facing the InCa Recon project is the creation of a set of hybrid algorithms based on various deep learning and image processing techniques to achieve this objective. This set of algorithms will constitute a complete technological solution for recognizing traffic signs and directional signs, a solution not currently available on the market or in the scientific literature.
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APPARCCO Project

The Apparcco project has been co-financed by the Ministry of Science, Innovation and Universities within the State Plan for Scientific and Technical Research and Innovation, in the 2017 Challenges-Collaboration call. File identification no.: RTC-2017-6555-4
The APPARCCO project was born with the goal of creating a platform that implements a dynamic pricing policy for parking, taking into account pollution and traffic levels in different areas and developing a parking space monitoring system that meets the cost and accuracy requirements necessary for the definitive takeoff of this market. APPARCCO will reduce emissions and congestion in cities through a dynamic pricing policy for on-street parking, which will be communicated to users before the start of their journey via an app integrated into a sustainable parking platform. This app will generate precise occupancy information using a radio frequency system, incorporate air quality and traffic data, estimate future occupancy, and quantify the benefits obtained using business intelligence tools.
ALL IN ONE Project
The All In One project has been co-financed by the Ministry of Economy and Competitiveness within the state R&D&I program, Challenges-Collaboration 2016 call. File identification number: RTC-2016-5479-4
All-in-One has developed an integrated, low-cost, and comprehensive traffic monitoring platform. This platform includes a vehicle counting radar and a Bluetooth identifier based on an innovative hardware and software integration architecture. The data provided by this new traffic sensor will generate a new level of traffic information by providing combined vehicle counting and identification measurements, including absolute origin-destination (OD) matrices and weighted travel times. This represents a significant advancement because a traffic manager will not only know, for example, the percentage of vehicles that will arrive at a point on the network at a given time (traditional OD matrix) but also their exact number (absolute OD matrix). This new information will be used and presented on GIS systems such as InCa, which will represent it geospatially, and will also include advanced visual tools to achieve the highest level of expressiveness of its content.

Integrated into InCa

All results can be viewed graphically from the InCa GIS
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Source/Destination Matrices

Visualization of Source-Destination matrices
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Interactive route selection

Combined graphical and alphanumeric display
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Capacity

IMD data with vehicle traceability and speed calculation
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Urbanet Project

The Urbanet project has been co-financed by the Ministry of Industry, Energy and Tourism, within the National Plan for Scientific Research, Development and Technological Innovation 2008-2011. File identification number: TSI-020100-2011-122
New paragraph URBANET: Integration of Information and Communication Technologies into Public Administration processes for the maintenance of urban infrastructure. The project's objective is to study the feasibility of a technological solution based on tablets and cloud computing to improve the efficiency of Public Administration processes for the management of urban elements. The objective is structured around the following main goals: Study of the capabilities of tablet devices for use as a field tool, and their differentiation from other mobile devices. Study of Public Administration processes and their adaptation for the use of tablets in fieldwork. Study of the possibilities of cloud computing and its use in a tablet-based solution for fieldwork. Study of the commercialization possibilities of tablet-based solutions for fieldwork. Implementation of a software tool on a tablet for a real-world fieldwork use case. As a general result, it is expected to lay the foundations for the development and commercialization of software tools for tablet devices, which will improve the processes of Public Administrations related to the management of urban elements by: Directly digitizing the information generated in the field, avoiding transcription errors, saving staff time, reducing paper consumption, and streamlining the updating of information in computer systems.

Project Accident0

The Accident0 project falls within the framework of grants for projects and actions under the Strategic Action for Telecommunications and the Information Society, co-financed by the Ministry of Industry, Tourism and Trade, within the National Plan for Scientific Research, Development and Technological Innovation 2008-2011. File identification number: TSI-020100-2009-735
  • ACCIDENT0 is a Cause Extraction Tool for Accidents (HECA), which allows for the generation of accurate accident models based on historical records. It includes:
  • Identification of relevant accident variables
  • Identification of complex (non-linear) interactions between these variables
  • Model inversion, that is, determining the set of variables that represent the main targets for reducing accidents, taking into account the complex relationships between them. We start with historical accident data, obtained using the form published in the Official State Gazette (BOE). We have more than 9,000 records and 82 different fields, which collect information on the road, environmental conditions, the circumstances of the accident, its severity, and any human factors that may have been involved. The variety of data types makes processing extremely difficult. Accident0 is integrated as a module into InCa, along with a set of tools for creating thematic maps, statistical queries, etc.
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    HECA application

    The HECA application is an Accident Cause Extraction Tool that allows for the generation of accurate accident models based on historical records. This application is launched directly from the InCa interface and enables various analyses of the accident data stored in the database.

    Descriptive Analysis of HECA

    These are basic statistical analyses of all the data or filtered sets of data: bar charts and joint distribution of variables. These tools allow for a simple visual analysis of the different variables involved in accidents.

    Overrepresentation

    It allows you to detect problems that in most cases could be solved by applying some type of control measure: increased safety measures, modification of road layouts, implementation of breathalyzer tests, etc. A simple example is alcohol: according to the analyzed data, Saturdays and Sundays are overrepresented in terms of accidents involving alcohol. Many other analyses are possible with this intuitive and powerful tool. Further explanation: the calculation is simple. In the example, if 20% of accidents without alcohol occur on weekends, but 40% of accidents involving alcohol occur on those same days, then alcohol is overrepresented by a factor of 40%/20% = 2 on weekends.

    Maximum Profit

    Given the overrepresentation of a risk factor, it's worth asking what the maximum number of accidents could be if a control measure were implemented to prevent it. This number is called the maximum gain, and it allows for a cost-benefit analysis of the design of countermeasures to be applied to reduce the factor. Further explanation: in the example of alcohol on weekends, it's unlikely that weekend accidents could be reduced by less than 20% (corresponding to the percentage of accidents in the absence of alcohol). More detail: to calculate the maximum gain, we first calculate the minimum number of accidents we could achieve, which corresponds to the percentage of weekend accidents out of all accidents without alcohol (20% in our example) multiplied by the total number of accidents with alcohol present. This number is the lower achievable limit. Therefore, the maximum gain is the number of alcohol-related accidents on weekends (the current situation) minus this lower achievable limit.

    Spatial Clustering

    The variables involved in a traffic accident have a spatial correlation that allows road segments to be classified according to profiles of these variables. For example, on certain roads, accidents tend to be concentrated in the early morning and afternoon, corresponding to the daily flow of people commuting to and from work, while in other areas, accidents tend to be concentrated in the early hours of weekend mornings. The innovative spatial clustering technique included in HECA allows for the local analysis, i.e., of road segments, of the occurrence of certain variables related to accidents. Each of the road segments considered is subsequently classified according to a typical accident profile. This analysis allows us to answer questions such as: On which segments do the most serious accidents occur? At what time of day and on which segments should we control speed to significantly reduce the number of accidents? Which areas have nighttime accident profiles?

    Correspondence Analysis

    Correspondence analysis creates a 2D representation of the different values that two variables can have, allowing us to discover relationships between the categories of the analyzed variables. For example, we can find a relationship between the injury severity variable (uninjured, minor, serious, fatal) and the road type variable. This analysis allows us to answer questions such as: What is the relationship between the injury severity of an accident and the time of day? At accident sites, is there a relationship between the road surface condition and the road type?

    Georeferencing

    All of InCa's statistical tools allow for the georeferencing of each accident in the database. This capability enables the creation of complex thematic maps based on user-selected filters. In this example, we show a thematic map that uses colors to differentiate accidents according to the quarter of the year in which they occurred.