Transporte

Este informe, que publica el Portal de Datos Europeo, analiza el potencial de reutilización de los datos en tiempo real. Los datos en tiempo real ofrecen información con alta frecuencia de actualización sobre el entorno que nos rodea (por ejemplo, información sobre el tráfico, datos meteorológicos, mediciones de la contaminación ambiental, información sobre riesgos naturales, etc.).

El documento resume los resultados y conclusiones de un seminario web organizado por el equipo del Portal de Datos Europeo celebrado el pasado 5 de abril de 2022, donde se explicaron diferentes formas de compartir datos en tiempo real desde plataformas de datos abiertos.

En primer lugar, el informe hace un repaso sobre el fundamento de los datos en tiempo real e incluye ejemplos que justifican el valor que aporta este tipo de datos para, a continuación, describir dos enfoques tecnológicos sobre cómo compartir datos en tiempo real del ámbito de IoT y el transporte. Incluye, además, un bloque que resume las principales conclusiones de las preguntas y comentarios de los participantes que giran, principalmente, en torno a difentes necesidades de fuentes de datos y funcionalidades requeridas para su reutilización.

Para terminar, basándose en el feedback y la discusión generada, se proporciona un conjunto de recomendaciones y acciones a corto y medio plazo sobre cómo mejorar la capacidad para localizar fuentes de datos en tiempo real a través del Portal de Datos Europeo.

Este informe se encuentra disponible en el siguiente enlace: "Datos en tiempo real: Enfoques para integrar fuentes de datos en tiempo real en data.europa.eu"

Open data portals are experiencing a significant growth in the number of datasets being published in the transport and mobility category. For example, the EU's open data portal already has almost 48,000 datasets in the transport category or Spain's own portal datos.gob.es, which has around 2,000 datasets if we include those in the public sector category. One of the main reasons for the growth in the publication of transport-related data is the existence of three directives that aim to maximise the re-use of datasets in the area. The PSI directive on the re-use of public sector information in combination with the INSPIRE directive on spatial information infrastructure and the ITS directive on the implementation of intelligent transport systems, together with other legislative developments, make it increasingly difficult to justify keeping transport and mobility data closed.

In this sense, in Spain, Law 37/2007, as amended in November 2021, adds the obligation to publish open data to commercial companies belonging to the institutional public sector that act as airlines. This goes a step further than the more frequent obligations with regard to data on public passenger transport services by rail and road.

In addition, open data is at the heart of smart, connected and environmentally friendly mobility strategies, both in the case of the Spanish "es.movilidad" strategy and in the case of the sustainable mobility strategy proposed by the European Commission. In both cases, open data has been introduced as one of the key innovation vectors in the digital transformation of the sector to contribute to the achievement of the objectives of improving the quality of life of citizens and protecting the environment.

However, much less is said about the importance and necessity of open data during the research phase, which then leads to the innovations we all enjoy. And without this stage in which researchers work to acquire a better understanding of the functioning of the transport and mobility dynamics of which we are all a part, and in which open data plays a fundamental role, it would not be possible to obtain relevant innovations or well-informed public policies. In this sense, we are going to review two very relevant initiatives in which coordinated multi-national efforts are being made in the field of mobility and transport research.

The information and monitoring system for transport research and innovation

At the European level, the EU also strongly supports research and innovation in transport, aware that it needs to adapt to global realities such as climate change and digitalisation. The Strategic Transport Research and Innovation Agenda (STRIA) describes what the EU is doing to accelerate the research and innovation needed to radically change transport by supporting priorities such as electrification, connected and automated transport or smart mobility.

In this sense, the Transport Research and Innovation Monitoring and Information System (TRIMIS) is the tool maintained by the European Commission to provide open access information on research and innovation (R&I) in transport and was launched with the mission to support the formulation of public policies in the field of transport and mobility.

TRIMIS maintains an up-to-date dashboard to visualise data on transport research and innovation and provides an overview and detailed data on the funding and organisations involved in this research. The information can be filtered by the seven STRIA priorities and also includes data on the innovation capacity of the transport sector.

If we look at the geographical distribution of research funds provided by TRIMIS, we see that Spain appears in fifth place, far behind Germany and France. The transport systems in which the greatest effort is being made are road and air transport, beneficiaries of more than half of the total effort.

 

However, we find that in the strategic area of Smart Mobility and Services (SMO), which are evaluated in terms of their contribution to the overall sustainability of the energy and transport system, Spain is leading the research effort at the same level as Germany. It should also be noted that the effort being made in Spain in terms of multimodal transport is higher than in other countries.

As an example of the research effort being carried out in Spain, we have the pilot dataset to implement semantic capabilities on traffic incident information related to safety on the Spanish state road network, except for the Basque Country and Catalonia, which is published by the General Directorate of Traffic and which uses an ontology to represent traffic incidents developed by the University of Valencia.

The area of intelligent mobility systems and services aims to contribute to the decarbonisation of the European transport sector and its main priorities include the development of systems that connect urban and rural mobility services and promote modal shift, sustainable land use, travel demand sufficiency and active and light travel modes; the development of mobility data management solutions and public digital infrastructure with fair access or the implementation of intermodality, interoperability and sectoral coupling.

The 100 mobility questions initiative

The 100 Questions Initiative, launched by The Govlab in collaboration with Schmidt Futures, aims to identify the world's 100 most important questions in a number of domains critical to the future of humanity, such as gender, migration or air quality.

One of these domains is dedicated precisely to transport and urban mobility and aims to identify questions where data and data science have great potential to provide answers that will help drive major advances in knowledge and innovation on the most important public dilemmas and the most serious problems that need to be solved.

In accordance with the methodology used, the initiative completed the fourth stage on 28 July, in which the general public voted to decide on the final 10 questions to be addressed. The initial 48 questions were proposed by a group of mobility experts and data scientists and are designed to be data-driven and planned to have a transformative impact on urban mobility policies if they can be solved.

In the next stage, the GovLab working group will identify which datasets could provide answers to the selected questions, some as complex as "where do commuters want to go but really can't and what are the reasons why they can't reach their destination easily?" or "how can we incentivise people to make trips by sustainable modes, such as walking, cycling and/or public transport, rather than personal motor vehicles?"

Other questions relate to the difficulties encountered by reusers and have been frequently highlighted in research articles such as "Open Transport Data for maximising reuse in multimodal route": "How can transport/mobility data collected with devices such as smartphones be shared and made available to researchers, urban planners and policy makers?"

In some cases it is foreseeable that the datasets needed to answer the questions may not be available or may belong to private companies, so an attempt will also be made to define what new datasets should be generated to help fill the gaps identified. The ultimate goal is to provide a clear definition of the data requirements to answer the questions and to facilitate the formation of data collaborations that will contribute to progress towards these answers.

Ultimately, changes in the way we use transport and lifestyles, such as the use of smartphones, mobile web applications and social media, together with the trend towards renting rather than owning a particular mode of transport, have opened up new avenues towards sustainable mobility and enormous possibilities in the analysis and research of the data captured by these applications.

Global initiatives to coordinate research efforts are therefore essential as cities need solid knowledge bases to draw on for effective policy decisions on urban development, clean transport, equal access to economic opportunities and quality of life in urban centres. We must not forget that all this knowledge is also key to proper prioritisation so that we can make the best use of the scarce public resources that are usually available to meet the challenges.

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Content written by Jose Luis Marín, Senior Consultant in Data, Strategy, Innovation & Digitalization.

The contents and views reflected in this publication are the sole responsibility of the author.

This report published by the European Data Portal (EDP) aims to help open data users in harnessing the potential of the data generated by the Copernicus program. 

The Copernicus project generates high-value satellite data, generating a large amount of Earth observation data, this is in line with the European Data Portal's objective of increasing the accessibility and value of open data. 

The report addresses the following questions, What can I do with Copernicus data? How can I access the data?, and What tools do I need to use the data? using the information found in the European Data Portal, specialized catalogues and examining practical examples of applications using Copernicus data.  

This report is available at this link: "Copernicus data for the open data community"

Spain was the second country in the world that received the most tourists during 2019, with 83.8 million visitors. That year, tourism activity represented 12.4% of GDP, employing more than 2.2 million people (12.7% of the total). It is therefore a fundamental sector for our economy.

These figures have been reduced due to the pandemic, but the sector is expected to recover in the coming months. Open data can help. Up-to-date information can bring benefits to all actors involved in this industry:

• Tourists: Open data helps tourists plan their trips, providing them with the information they need to choose where to stay or what activities to do. The up-to-date information that open data can provide is particularly important in times of COVID. There are several portals that collect information and visualisations of travel restrictions, such as the UN's Humanitarian Data Exchange. This website hosts a daily updated interactive map of travel restrictions by country and airline.
• Businesses. Businesses can generate various applications targeted at travellers, with useful information. In addition, by analysing the data, tourism establishments can detect untapped markets and destinations. They can also personalise their offers and even create recommendation systems that help to promote different activities, with a positive impact on the travellers' experience.
• Public administrations. More and more governments are implementing solutions to capture and analyse data from different sources in real time, in order to better understand the behaviour of their visitors. Examples include Segovia, Mallorca and Gran Canaria. Thanks to these tools, they will be able to define strategies and make informed decisions, for example, aimed at avoiding overcrowding. In this sense, tools such as Affluences allow them to report on the occupation of museums, swimming pools and shops in real time, and to obtain predictions for successive time slots.

The benefits of having quality tourism-related data are such that it is not surprising that the Spanish Government has chosen this sector as a priority when it comes to creating data spaces that allow voluntary data sharing between organisations. In this way, data from different sources can be cross-referenced, enriching the various use cases.

The data used in this field are very diverse: data on consumption, transport, cultural activities, economic trends or even weather forecasts. But in order to make good use of this highly dynamic data, it needs to be available to users in appropriate, up-to-date formats and access needs to be automated through application programming interfaces (APIs).

Many organisations already offer data through APIs. In this infographic you can see several examples linked to our country at national, regional and local level. But in addition to general data portals, we can also find APIs in open data platforms linked exclusively to the tourism sector. In the following infographic you can see several examples:

Click here to see the infographic in full size and in its accessible version.

Do you know more examples of APIs or other resources that facilitate access to tourism-related open data? Leave us a comment or write to datos.gob.es!

Content prepared by the datos.gob.es team.

Mobility is a key economic driver. Increasing the efficiency and quality of a country's mobility system contributes both to the strength of its economy and to improving the quality of life of its citizens. This is particularly important in the mobility systems of cities and their metropolitan areas, where most of the population and, thus, most of the economic activity is concentrated.

Aware of this - and because we citizens demand it - local authorities have for decades allocated a significant part of their annual resources to expanding, improving and making their transport and mobility networks more efficient.

In the last decade, open data has been one of the most important vectors of innovation that have been introduced in the mobility strategies developed by cities, giving rise to initiatives that would have been difficult to imagine in previous periods. Despite all the complexities involved, opening both static and real-time mobility datasets for reuse is actually cheap and simple compared to the cost of building a new transport infrastructure or the cost of acquiring and maintaining the operational support systems (OSS) associated with mobility services. In addition, the existence of an increasing deployment of sensor networks, accessible through control systems deployed in the context of "smart city" strategies, makes the task a little easier.

We should not forget, moreover, that public transport is key to tackling climate change as it is one of the fastest growing sources of greenhouse gas emissions, and public transport offers the best mobility solution to move people quickly and efficiently in cities around the world. As shown in the figure, simply shifting passengers using their private vehicles to public transport has a major impact on reducing greenhouse gas emissions. The Bus Industry Confederation estimates that shifting passengers from cars to public transport can lead to a 65% reduction in emissions during peak hours. This reduction could be as high as 95% in emissions during off-peak hours for those commuters who switch from private cars to public transport.

For all these reasons, there are already numerous examples where freeing up transport and mobility data to put it in the hands of travellers is proving to be a policy with important benefits for many cities: it allows better use of resources and contributes to more efficient mobility in urban space.

Let's look at some examples that may not be as well-known as the ones that usually reach the media, but which demonstrate how the release of data allows for innovations that benefit both users and, in some cases, the authorities themselves.

Redesigning New York City bus routes

All cities are constantly thinking of ways to improve their bus routes in order to provide the best possible service to citizens. In New York City, however, the open data policy, as an unplanned consequence, provided an important aid to the authorities, based on the analysis of data from the bus network users themselves.

The rider-driven Bus Turnaround Coalition campaign, supported by TransitCenter, a foundation working to improve public transport in US cities, and the Riders Alliance, is using open data to raise awareness about the state of New York City's bus network, proposing solutions for improvement to the Metropolitan Transportation Authority (MTA).

To formulate their recommendations, the organisations analysed bus arrival times using the MTA's own location maps, incorporated real-time data through the GTFS specification, reviewed ridership data, and mapped (and optimised) bus routes.

Among the most innovative proposals is the shift in approach to route design criteria. Instead of trying to cater to all types of travellers, the Bus Turnaround Coalition, after analysing how people actually move around the city and what type of transport they would need to achieve their goals efficiently, proposed the following recommendations:

• Add lines to take passengers from the outskirts of the city directly to the underground lines, facilitating a quick trip.
• Improve lines to offer short, fast routes within a neighbourhood for people who want to run a quick errand or visit a close friend.
• Split routes that are too long to minimise the risk of delays.
• Readjust the distance between stops, which are often too close together, complementing gaps in metro coverage.

Open data has turned frequent rider protests and complaints about poor network performance into a set of reasoned, data-driven inputs, which have been captured in a series of MTA commitments to improve New York's bus network, such as redesigning the network by 2021, increasing journey speeds by 25%, and proactively managing bus maintenance.

Bicycle usage data in San Francisco

Like many other cities, San Francisco, through its Municipal Transportation Agency (SFMTA), records travel data from users of its public bike-sharing system and makes it available as open data. In this case, the transport authority itself publishes regular reports, both on the overall use of the system and on the conclusions it draws for the improvement of the city's own mobility.

By documenting and analysing the volumes and trends of bicycle use in San Francisco, they are able to support the goals of the SFMTA's Strategic Plan, which aims to prioritise other forms of travel in the city than the private car.

For example, ongoing analysis of bicycle passenger volumes at key intersections in the city and citizen input has reduced traffic congestion and accidents by re-prioritising vehicle traffic priorities according to actual roadway usage at any given time of day.

Efficient parking in Sacramento

Many cities try to address traffic congestion problems from different perspectives including efficient parking management. Therefore, one of the datasets frequently published by cities with open data initiatives is public parking occupancy.

In the city of Sacramento, California, the open data initiative publishes datasets from the citywide sensor network that monitors parking availability at parking meters and not only in the city's public car parks. In this way they have managed to reduce emissions as vehicles spend less time looking for parking, while significantly improving traffic flow and the satisfaction of citizens using the Sacpark app.

In 2020, due to the pandemic, passenger transport around the world was drastically reduced due to the mobility restriction policies that governments around the world had to deploy to curb the spread of the virus, as seen in the image below.

In June 2021 cities are still far from recovering the levels of mobility they had in March 2020, but we continue to make progress in making data the basis on which to build useful information, and essential in the new innovations coming through artificial intelligence.

So, as the pandemic recedes, and many initiatives resume, we continue to see how open data is at the heart of smart, connected and environmentally friendly mobility strategies.

Content prepared by Jose Luis Marín, Senior Consultant in Data, Strategy, Innovation & Digitalization.

The contents and views reflected in this publication are the sole responsibility of the author.

A symptom of the maturity of an open data ecosystem is the possibility of finding datasets and use cases across different sectors of activity. This is considered by the European Open Data Portal itself in its maturity index. The classification of data and their uses by thematic categories boosts re-use by allowing users to locate and access them in a more targeted way. It also allows needs in specific areas to be detected, priority sectors to be identified and impact to be estimated more easily.

In Spain we find different thematic repositories, such as UniversiData, in the case of higher education, or TURESPAÑA, for the tourism sector. However, the fact that the competences of certain subjects are distributed among the Autonomous Communities or City Councils complicates the location of data on the same subject.

Datos.gob.es brings together the open data of all the Spanish public bodies that have carried out a federation process with the portal. Therefore, in our catalogue you can find datasets from different publishers segmented by 22 thematic categories, those considered by the Technical Interoperability Standard.

Number of datasets by category as of June 2021

But in addition to showing the datasets divided by subject area, it is also important to show highlighted datasets, use cases, guides and other help resources by sector, so that users can more easily access content related to their areas of interest. For this reason, at datos.gob.es we have launched a series of web sections focused on different sectors of activity, with specific content for each area.

4 sectorial sections that will be gradually extended to other areas of interest

Currently in datos.gob.es you can find 4 sectors: Environment, Culture and leisure, Education and Transport. These sectors have been highlighted for different strategic reasons:

• Environment: Environmental data are essential to understand how our environment is changing in order to fight climate change, pollution and deforestation. The European Commission itself considers environmental data to be highly valuable data in Directive 2019/1024. At datos.gob.es you can find data on air quality, weather forecasting, water scarcity, etc. All of them are essential to promote solutions for a more sustainable world.
• Transport: Directive 2019/1024 also highlights the importance of transport data. Often in real time, this data facilitates decision-making aimed at efficient service management and improving the passenger experience. Transport data are among the most widely used data to create services and applications (e.g. those that inform about traffic conditions, bus timetables, etc.). This category includes datasets such as real-time traffic incidents or fuel prices.
• Education: With the advent of COVID-19, many students had to follow their studies from home, using digital solutions that were not always ready. In recent months, through initiatives such as the Aporta Challenge, an effort has been made to promote the creation of solutions that incorporate open data in order to improve the efficiency of the educational sphere, drive improvements - such as the personalisation of education - and achieve more universal access to knowledge. Some of the education datasets that can be found in the catalogue are the degrees offered by Spanish universities or surveys on household spending on education.
• Culture and leisure: Culture and leisure data is a category of great importance when it comes to reusing it to develop, for example, educational and learning content. Cultural data can help generate new knowledge to help us understand our past, present and future. Examples of datasets are the location of monuments or listings of works of art.

Structure of each sector

Each sector page has a homogeneous structure, which facilitates the location of contents also available in other sections.

It starts with a highlight where you can see some examples of outstanding datasets belonging to this category, and a link to access all the datasets of this subject in the catalogue.

It continues with news related to the data and the sector in question, which can range from events or information on specific initiatives (such as Procomún in the field of educational data or the Green Deal in the environment) to the latest developments at strategic and operational level.

Finally, there are three sections related to use cases: innovation, reusing companies and applications. In the first section, articles provide examples of innovative uses, often linked to disruptive technologies such as Artificial Intelligence. In the last two sections, we find specific files on companies and applications that use open data from this category to generate a benefit for society or the economy.

Highlights section on the home page

In addition to the creation of sectoral pages, over the last year, datos.gob.es has also incorporated a section of highlighted datasets. The aim is to give greater visibility to those datasets that meet a series of characteristics: they have been updated, are in CSV format or can be accessed via API or web services.

What other sectors would you like to highlight?

The plans of datos.gob.es include continuing to increase the number of sectors to be highlighted. Therefore, we invite you to leave in comments any proposal you consider appropriate.

Artificial intelligence is transforming companies, with supply chain processes being one of the areas that is obtaining the greatest benefit. Its management involves all resource management activities, including the acquisition of materials, manufacturing, storage and transportation from origin to final destination.

In recent years, business systems have been modernized and are now supported by increasingly ubiquitous computer networks. Within these networks, sensors, machines, systems, vehicles, smart devices and people are interconnected and continuously generating information. To this must be added the increase in computational capacity, which allows us to process these large amounts of data generated quickly and efficiently. All these advances have contributed to stimulating the application of Artificial Intelligence technologies that offer a sea of ​​possibilities.

In this article we are going to review some Artificial Intelligence applications at different points in the supply chain.

Technological implementations in the different phases of the supply chain

Planning

According Gartner, volatility in demand is one of the aspects that most concern entrepreneurs. The COVID-19 crisis has highlighted the weakness in planning capacity within the supply chain. In order to properly organize production, it is necessary to know the needs of the customers. This can be done through techniques of predictive analytics that allow us to predict demand, that is, estimate a probable future request for a product or service. This process also serves as the starting point for many other activities, such as warehousing, shipping, product pricing, purchasing raw materials, production planning, and other processes that aim to meet demand.

Access to real-time data allows the development of Artificial Intelligence models that take advantage of all the contextual information to obtain more precise results, reducing the error significantly compared to more traditional forecasting methods such as ARIMA or exponential smoothing.

Production planning is also a recurring problem where variables of various kinds play an important role. Artificial intelligence systems can handle information involving material resources; the availability of human resources (taking into account shifts, vacations, leave or assignments to other projects) and their skills; the available machines and their maintenance and information on the manufacturing process and its dependencies to optimize production planning in order to satisfactorily meet the objectives.

Production

Within of the stages of the production process, one of the stages more driven by the application of artificial intelligence is the quality control and, more specifically, the detection of defects. According to European Comission, 50% of the production can end up as scrap due to defects, while, in complex manufacturing lines, the percentage can rise to 90%. On the other hand, non-automated quality control is an expensive process, as people need to be trained to be able to perform the inspections properly and, furthermore, these manual inspections could cause bottlenecks in the production line, delaying delivery times. Coupled with this, inspectors do not increase in number as production increases.

In this scenario, the application of computer vision algorithms can solve all these problems. These systems learn from defect examples and can thus extract common patterns to be able to classify future production defects. The advantages of these systems is that they can achieve the precision of a human or even better, since they can process thousands of images in a very short time and are scalable.

On the other hand, it is very important to ensure the reliability of the machinery and reduce the chances of production stoppage due to breakdowns. In this sense, many companies are betting on predictive maintenance systems that are capable of analyzing monitoring data to assess the condition of the machinery and schedule maintenance if necessary.

Open data can help when training these algorithms. As an example, the Nasa offers a collection of data sets donated by various universities, agencies or companies useful for the development of prediction algorithms. These are mostly time series of data from a normal operating state to a failed state. This article shows how one of these specific data sets (Turbofan Engine Degradation Simulation Data Set, which includes sensor data from 100 engines of the same model) can be taken to perform a exploratory analysis and a model of linear regression reference.

Transport

Route optimization is one of the most critical elements in transportation planning and business logistics in general. Optimal planning ensures that the load arrives on time, reducing cost and energy to a minimum. There are many variables that intervene in the process, such as work peaks, traffic incidents, weather conditions, etc. And that's where artificial intelligence comes into play. A route optimizer based on artificial intelligence is able to combine all this information to offer the best possible route or modify it in real time depending on the incidents that occur during the journey.

Logistics organizations use transport data and official maps to optimize routes in all modes of transport, avoiding areas with high congestion, improving efficiency and safety. According to the study “Open Data impact Map”, The open data most demanded by these companies are those directly related to the means of transport (routes, public transport schedules, number of accidents…), but also geospatial data, which allow them to better plan their trips.

In addition, exist companies that share their data in B2B models. As stated in the Cotec Foundation report “Guide for opening and sharing data in the business environment”, The Spanish company Primafrio, shares data with its customers as an element of value in their operations for the location and positioning of the fleet and products (real-time data that can be useful to the customer, such as the truck license plate, position, driver , etc.) and for billing or accounting tasks. As a result, your customers have optimized order tracking and their ability to advance billing.

Closing the transport section, uOne of the objectives of companies in the logistics sector is to ensure that goods reach their destination in optimal conditions. This is especially critical when working with companies in the food industry. Therefore, it is necessary to monitor the state of the cargo during transport. Controlling variables such as temperature, location or detecting impacts is crucial to know how and when the load deteriorated and, thus, be able to take the necessary corrective actions to avoid future problems. Technologies such as IoT, Blockchain and Artificial Intelligence are already being applied to these types of solutions, sometimes including the use of open data.

Customer service

Offering good customer service is essential for any company. The implementation of conversational assistants allows to enrich the customer experience. These assistants allow users to interact with computer applications conversationally, through text, graphics or voice. By means of speech recognition techniques and natural language processing, these systems are capable of interpreting the intention of users and taking the necessary actions to respond to their requests. In this way, users could interact with the wizard to track their shipment, modify or place an order. In the training of these conversational assistants it is necessary to use quality data, to achieve an optimal result.

In this article we have seen only some of the applications of artificial intelligence to different phases of the supply chain, but its capacity is not only limited to these. There are other applications such as automated storage used by Amazon at its facilities, dynamic prices depending on the demand or the application of artificial intelligence in marketing, which only give an idea of ​​how artificial intelligence is revolutionizing consumption and society.

Content elaborated by Jose Antonio Sanchez, expert in Data Science and enthusiast of the Artificial Intelligence.

Contents and points of view expressed in this publication are the exclusive responsibility of its author.

Open mobility data plays a key role in transforming current transport networks and systems, promoting their digitization and improving their sustainability and efficiency. The European Union is aware of this situation, and for this reason it has not only included mobility data among the categories considered of high value in the directive (EU) 2019/1024, but also highlighted its importance in the new "Sustainable and smart mobility strategy", including lines of action related to its impulse, how we approach previously.

In this new article we are going to focus on the situation in Spain, where measures related to open data have also been included in the national mobility strategy.

The Secure, Sustainable, Connected Mobility Strategy 2030

The Secure, Sustainable, Connected Mobility Strategy 2030 (es.movilidad) published by the Ministry of Transport, Mobility and Urban Agenda (MITMA) in September 2020 recognizes the importance of open data in the process of digitization and automation of transport, as well as the regulatory challenges posed by collection conditions, transfer and access to the data generated in the different parts of the value chain. For this reason, the strategy proposes that a future Law on Sustainable Mobility and Transport Financing addresses these issues, offering solutions that eliminate barriers to the opening of data andwhat guarantee the privacy of users and the protection of different business strategies.

Although they are present in other measures, in axis 5 (Intelligent Mobility) of the Strategy there are four measures, three of them within the line of action for the Facilitation of Mobility as a Service, Open Data and New Technologies for Analysis and Optimization of Mobility, in which open data explicitly plays a prominent role:

• To the extent designed to promote the publication of open mobility data from MITMA in coordination with the data.gob.es platform aims to adopt a proactive role in making open data available (both planned and in real operation) that are relevant to society.
• The implementation of the National Data Access Point for multimodal travel aims to create a single repository of open transport data (schedules, fares, routes, geographical coordinates of stops, etc.) provided by transport authorities, operators, providers of shared mobility services or transport services on demand, infrastructure managers, etc. anyway at the national level. In this sense, it should be noted that MITMA intends to go beyond the mandatory minimum and create three other national access points (real-time traffic, safety information in relation to traffic and safe parking for freight transport).
• To promote the development of mobility applications, guaranteeing the availability of quality and real-time data, MITMA will analyze the convenience of promoting complementary regulation so that all transport operators, infrastructure managers, and transport service providers on demand. provide dynamic, reliable and real-time data of their transport services to be made available to third parties.
• Finally the design and implementation of the SIMPLE technology platform, also foresees the reuse of information throughout the logistics and transport chain, based on the principle of providing unique data only once. This platform will allow to know the traceability of goods in the different means of transport and, on the other hand, will allow the interconnection between the Public Administration and the different agents of the private logistics sector to facilitate trade and transport, nationally and internationally.

These measures are summarized in the following image:

On the other hand, a request for expressions of interest to identify proposals for the integration of artificial intelligence in the value chains of the economy in order to promote the digital transformation of the economic fabric, within the framework of Recovery Plan. And one of the five axes on which one's own National Artificial Intelligence Strategy (ENIA) recognizes the impact of AI and data is of course, sustainable and smart mobility. It should be noted that it is an invitation aimed at projects in the phases closest to the market of the innovation process based on medium to high maturity technologies (TRL 6 onwards) as a complement to R&D support actions.

Now that the pandemic period is coming to an end, the economic recovery effort opens up fascinating opportunities for innovation and digital transformation in sectors where the penetration of artificial intelligence and the use of data so far has been much lower than the sector of the information technologies, something that not only happens in mobility and transport but also in the farming, energy or health and education.

We are therefore faced with a unique opportunity that we cannot afford to miss; which is also accompanied by a significant boost in the form of public financing and in which transport and mobility stand out due to their impact not only on the economy, but also on the environment and on the quality of life of citizens.

Content written by Jose Luis Marín, Senior Consultant in Data, Strategy, Innovation & Digitalization.

The contents and points of view reflected in this publication are the sole responsibility of its author.

More than two years ago we started 2019 very hopeful about the progress that was being made in Spain regarding the openness of data related to transport and mobility, after a few years in which there had not been much progress. Although there is still room for improvement, especially in the availability of open data in real time, the fact that applications in the transport category already represent 20% of the total published on the datos.gob.es portal serves as an indicator of progress in this period.

In these two years, the pace of innovation in everything related to the use of data and artificial intelligence has been accelerated not only by technological progress, but also by a significant legislative impulse, both at European and national level. For example, the new Directive (EU) 2019/1024 considers data related to mobility and transport to be among the six groups declared to be of high value for their considerable benefits to society, the environment and the economy. Therefore, their momentum has been considered in the new mobility strategy.

European framework for mobility data

The European data strategy published in 2020 has among its objectives to create a single data market that ensures Europe's global competitiveness and data sovereignty through the creation of common European data spaces in nine strategic sectors, capable of ensuring that more data are available for use in the economy and society. Actions leading to the development of these data spaces are being channelled through the different strategies that the European Commission is developing to deliver on the priorities set for the period 2019-2024. Some examples already under development are the common manufacturing data space or the common agricultural data space.

As regards transport, in December 2020 the European Commission presented its "Sustainable and Smart Mobility Strategy" accompanied by an action plan of 82 initiatives for the next 4 years that will contribute to achieving the objectives of the European Green Pact. This strategy lays the groundwork for how the EU transport system must achieve its green and digital transformation to become more resilient to future crises.

Although the role of data is present in most of the points, among the ten key actions there is one that focuses exclusively on the role of data. In Action 7: "Innovation, data and artificial intelligence for smarter mobility", in addition to the objectives related to fostering innovation in general and building adequate digital infrastructures, the following points related to data and artificial intelligence stand out:

• The Commission stresses the need to step up efforts related to data availability, access and exchange in order to make the digital transformation of the transport and mobility sector a reality.
• It recognises that the availability of data and statistics is also essential, especially real-time data, as it enables better services to citizens or transparency of supply chains in freight transport.
• The need to remove barriers such as unclear regulatory conditions, the absence of an EU market for data provision, the lack of an obligation to collect and share data or misgivings about data sovereignty, among others, is highlighted.
• The commitment to propose further actions to build a common European data space for mobility data, set out in the Data Strategy, is developed. This sets the objective of collecting, connecting and making data available to achieve the objectives of sustainability and multimodality.
• Of particular relevance is the commitment that the mobility data space should work in synergy with other key systems such as energy, satellite navigation and telecommunications.
• It deals in a very differentiated way with access to vehicle data where the Commission is committed to present a new initiative on access to vehicle data, through which it will propose a balanced framework to ensure fair and efficient access to vehicle data for mobility service providers.
• The Commission plans to fund research, innovation and deployment of transport solutions based on artificial intelligence through the Horizon Europe and Digital Europe programmes, recognising that artificial intelligence is central to the automation of transport in all its modes. In this context, the Commission will support test and experimentation centres dedicated to AI for smart mobility.

This is certainly a very ambitious set of commitments that must also be compatible with EU data protection rules and ensure a level playing field for data in the value chain, so that innovation can flourish and new business models emerge. Otherwise operators would perceive that the common mobility data space is not secure and reliable for sharing their data and it would be very difficult to meet the ambitious targets that already by 2030 aim for automated mobility to be deployed on a large scale and for multimodal passenger transport to be a reality supported by integrated e-ticketing.

Contenido elaborado por Jose Luis Marín, Senior Consultant in Data, Strategy, Innovation & Digitalization.

The contents and views expressed in this publication are the sole responsibility of the author.

Data related to mobility and transport are among the most demanded by users and re-users. The European Union itself considers them as high-value data, according to the preliminary classification included in Directive (EU) 2019/1024, due to their "considerable benefits for society, the environment and the economy".

The opening and re-use of transport data brings benefits for all:

• Public bodies. Detailed information, often in real time, helps public authorities to make decisions aimed at efficient infrastructure management and service improvement. It also helps to reduce costs, as demonstrated several years ago in the UK’s Transport for London (ODImpact) study by the GovLab.
• Private transport companies. Private public transport operators, mobility service providers and private infrastructure managers also need reliable information to understand the different variables that affect their business and to act accordingly, adapting their services to users' needs.
• Businesses and re-users. The openness of transport data stimulates economic growth. There is a huge market for applications based on transport data. You only need to take a look at the applications section of our portal to see that almost 20% of the applications highlighted are related to this category: applications that provide information on traffic conditions, public service timetables, the best routes to reach the requested destination, etc.
• Citizens. Thanks to these solutions, citizens can, for example, plan their journey in advance, thus saving time, and benefit from more efficient, accessible and sustainable transport.

What kind of data related to mobility and transport can I find in datos.gob.es?

At datos.gob.es we have an extensive catalogue of data related to mobility. There are currently 1,820 datasets grouped under a category called "Transport", following the cataloguing rules set out in the Technical Interoperability Standard (NTI, in its Spanish acronym). Based on this standard, this category includes communications and traffic datasets, such as data on traffic control, registrations, accidents, civil aviation, land transport (road and rail) or merchant navy, among others.

Most of these datasets are in CSV (1,333) and JSON (1,113) format, facilitating their re-use. There are state, regional and local data. The publishers that share the most data of this type are the National Statistics Institute (INE), Gijón City Council, the Autonomous Community of the Basque Country and Madrid City Council.

The most important of these datasets are listed below, together with the format in which they can be consulted:

At the state level

• INE. Movement of people by mobility areas. Daily. National. CSV, XLSX, XLS, HTML, PC-Axis, JSON.
• Port Authority of Barcelona. Railway services of the Port of Barcelona. RDF-XML.
• Port Authority of the Balearic Islands. Maritime traffic forecast. CSV, HTML.
• Renfe – Operator. List of Alta Velocidad, Larga Distancia and Media Distancia stations. CSV, XLSX.
• Central Trafficking Office. Driver census. CSV, XLS.
• Central Trafficking Office. Traffic accidents with victims. XLS

At Autonomous Community level

• Autonomous Community of the Canary Islands. Vehicles registered according to type of vehicle and services by Canary Islands and time periods.  HTML, JSON, PC-Axis.
• Basque Government. Transport and mobility resources in the Basque Country. KML, Api, GeoJSON, XML, RSS, JSON, XLSX.
• Government of Catalonia. Monthly electrified and hybrid vehicle registrations in Catalonia. XML-APP, CSV, JSON, RDF-XML.
• Government of Catalonia. Real time road incidents in Catalonia. RSS, XML-APP.
• Government of the Balearic Islands. Mallorca Car Rental Agencies. RDF-XML, CSV, XML-APP, JSON.
• Government of Aragon. Mobility analysis with Big Data. CSV, JSON, XLS, XML-APP.
• Castilla y Leon Government. Statistics on passenger cars. CSV, RDF-XML, JSON.
• Andalusian Government. Data from the Andalusian Network of Transport Consortiums. JSON.
• Government of Valencia. GTFS of itineraries and timetables of intercity public transport services by bus. ZIP.

At local level

• Madrid City Council. Bicycle. Quiet streets. KML, ZIP, XLS, CSV.
• La Palma Island Council. Road and track infrastructures. HTML, JSON.
• Barcelona City Council. Surface parking spots rates' information in the city of Barcelona. CSV.
• Santiago de Compostela City Council. Real time traffic observations. CSV, GML, JSON.
• Alcoy City Council. Charging points. KML, ZIP, JSON.
• Terrasa City Council. Petrol stations. CSV.
• Santander City Council. TUS sales/recharge points. HTML, RDF-XML, CSV, RDF-Turtle, RDF-N3, JSON, XML-APP, JSON-LD, Atom.
• Caceres City Council. Caceres Monumental City car park. XLS, GeoJSON, JSON, RDF-Turtle, CSV, SPARQL.
• Pamplona City Council. Car park occupancy in real time. XML-APP.

In addition, the Ministry of Transport, Mobility and Urban Agenda is publishing new datasets. Specifically, the data on mobility in Spain during the COVID-19 pandemic at national level. These data, which are now openly available on the Ministry's website, are the result of the Mobility Study with Big Data carried out to assess the effectiveness of the mobility restriction measures adopted during the state of alarm and to support decision-making.

Some examples of re-use of mobility-related data

As we saw earlier, data related to mobility is a great raw material for the impulse of ingenuity and creativity in the form of new products and services. The best known are the applications that provide information on the timetables of public transport services or the most suitable routes and means of transport for each journey, but there are many more.

In our section on reusing companies we find some organisations that have taken advantage of this data to develop new businesses. One example is Canard Drones, which develops and markets solutions for the verification and calibration of navigation aids (NAVAID) and runway inspection, using public data. Another example is remOT Technologies and its RuralMaps application, which facilitates GPS navigation on roads in rural environments by indicating the optimal and fastest route to reach the desired destination, saving cost and time for agricultural technicians and emergency services.

Transport data can also be used to enrich tourism applications and improve the experience of visitors, who can find all the information they need to enjoy their stay (hotels, transport, points of interest, etc.) on a single platform. Or they can help to improve the architecture of cities: the data collected can be used to define spaces that are more suitable for the common good in cities, for example, the necessary width of pavements.

With the rise of smart cities, with IoT devices collecting data to manage assets and resources efficiently, we can expect more and more mobility data to become available. Data that can be queried, enriched with new data, and used to create valuable applications and services.

We live in a world where travellers' needs and habits are constantly evolving, and there is a growing demand for efficiency and flexibility in mobility options. Open data can help respond to this demand, while driving economic development.