MobiDataLab - Labs for prototyping future mobility data sharing solutions
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| Number of Parts | 351 | |
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| License | CC Attribution 3.0 Unported: You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor. | |
| Identifiers | 10.5446/69148 (DOI) | |
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| Production Year | 2022 |
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00:00
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Transcript: English(auto-generated)
00:00
Great, thank you for the kind introduction, so I'm really happy to be here at a conference, physical conference, to see people alive and not just behind screens, and I'm quite happy to present our project Mobi Data Lab, Labs for Prototyping Future Mobility Data Sharing Solutions, I think a quite important topic, so from the European Commission presentations
00:26
now to an H2020 finance project, shortly about the project, it is funded within the H2020 context, mobility for growth, and then 2018 to 2020 call of the European Union, we did
00:44
set up a number of partners, so 10 partners, I will name them here, it's ACCA, now ACORDIS, Aetern from Crease, CNR, nearby from Pisa, the F6F from Ireland, we here, I-Corps, Kizzo
01:04
now named Earth from France, the KU Leuven, the legal part, Polis, and University from Spain, the URV, so the project did start on February 2021, just in the midst of the
01:26
pandemic, and it will end in 2024, so we are roughly half-time on the project, so about mobility data sharing, the challenge, yeah, we all know how important data sharing
01:42
is, data is the goal we are all working with, and it's super important, we know all the mobility services, they are appearing in both in the public and the private sector, and there is more and more data being produced at a higher pace in various forms, data almost
02:02
everywhere, when you're booking a travel to here to this conference, you have a couple of portals where you're producing data, where you're using data, and so on, so I would assume everybody agrees that data sharing is quite important, it can unlock new insights and should lead to more efficient processes and new products, but there's still a lot
02:27
of reluctance to share data, there will be blocker, and the barriers, yeah, put in here six of them, the main concerns are privacy concerns, for sure, so not everybody
02:40
wants to share travel data, movement data, positional data, ticketing data, and so on, for companies, there's the risk of losing competitive advantage, so when you have, when you're at the market to share data, that could mean that you're showing
03:02
some further insights into your business case, then there is a technical gap and lack of cloud solutions, I think that's doable, we saw all in the past years that there are a lot of cloud solutions are coming up, but the specific mobility cloud solutions
03:21
are not there, so there's still a lack of that, and we do have the regulatory compliance, could be also barrier, so from the political point of view, then there is also a fear of losing control of data, so of your data, data from individuals, or data from companies that are
03:43
sharing the data that, for example, another company can take this data and build a use case on top of that, and last but not least, the interoperability issues, yeah, to make data interoperable and make it easy to connect the services and the data,
04:06
our path to the solution is based on four pillars, so the state of the art, we are building an open knowledge base to share knowledge and mobility data, it's a kind of a theoretical study, we are providing this knowledge via our mobidatalab.eu
04:25
web page in an open knowledge base about legal stuff, about data, privacy and governance stuff, technical standards, and further information regarding mobility data and the things that you need to be aware to share this data, then the second pillar is the proof of value,
04:46
we will initiate a transport cloud, this is an implementation, so this will be an EU-wide portal of mobility open data to access this data for public transport data, road transport
05:02
data in selected areas together with our reference group members and people institutions that want to be part of this initiative, data about weather, pollution data and further open data which is relevant for the mobility sector, we will put that into practice in the third pillar in living and virtual labs,
05:27
so the organizing labs and events, hackathons, codagons, datatons, city challenges and we are connecting with companies, startups, people that are acting in this space to figure
05:45
out best ways to solve this mobility sharing challenges and last but not least in the fourth pillar we will assess the impact to give a clear assessment of the project, we initiate an
06:00
evaluation framework where we can rate our project, we did some market analysis, provided business models, intellectual property is for sure an important topic and further things, so stepping into the single pillars that I described briefly on the slide before,
06:22
the open knowledge base gives you further information and legal and governance things, so what are the legal things that you need to be compliant to share this data, what is important for data suppliers, for data users when they are working with the
06:44
data from a legal point of view, then about data privacy we are providing some guidance on data anonymization, on privacy by design, providing methodologies to apply that on the data and so forth, the standards topic, so we'll come to that back later in a more detail,
07:05
there are a lot of standards and the data is super heterogeneous so you need to be aware of the standards and you need to have fitting standards and probably new standards will evolve to make this data fitting, further cloud solution frameworks to adapt clouds, the existing
07:24
clouds to being able to handle this mobility data and last but not least the use cases that are quite important that we did build with our reference group members that are working on real world use cases, use cases such as emission reporting, a better estimated time of arrival,
07:48
the analysis of linked open data parts and doing analytics and visualization of data at all. The proof of value is then the practical implementation the transport cloud,
08:04
so it's part of that is the cloud federation, we do have a reference data catalog so it's quite important to make the data fair, findable, accessible, interoperable and reusable.
08:26
An overview on data access services, so it's not just about the data it's also about services, data is the one part but just with data you cannot do anything, you need to have services
08:40
that will help you to do this mobility challenges, to solve these challenges. And then the fourth part here is data privacy about the anonymization, so we are applying algorithms, one part of the project is doing research in this field and using this project
09:01
to apply this most recent research in our transport cloud. Further we will have data processors about geographic enrichment, you have different data sets you will connect this data sets, you link this data sets, you enrich that with further data,
09:20
also licenses are topics or you are not allowed to do each and everything with each and every data set you will have. It's about semantic enrichment, so the whole part of RDF sparkle and linked open data and the processes are quite open to be or get extended
09:43
with other enrichment processes based on the demands of the reference group and this project stakeholders. We will put this into practice in living in virtual labs as I mentioned with our two partners Polis and F6S, one from Brussels, Belgium, the other one
10:05
from Ireland, we do have connections to a large network of cities and regions that are also data providers to SMEs and startups and data users, so those will participate,
10:22
provide challenges for our living and virtual labs. So this is then the part where we are doing the practice after the theoretical discussions, the implementation, we will bring that up to life with our reference group members, you see here on the right part of
10:45
the screen there are a couple of our reference group members that are really providing mobility solutions, they're providing data and we will implement this together with them and having feedback from them to figure out what would be good solutions for mobility data sharing.
11:04
Then this impact assessment is also something to justify the project investments. We did one couple of surveys within the project, we did a market analysis to figure out what's already there
11:21
whether from a proprietary solutions in the open source market or further mobility data sharing providers, we did a gap analysis and we provided business and revenue models. Overall we also did introduce the data sharing assessment framework, all this generated
11:44
knowledge here is available on our website, so these documents are public so we can do a brief look at this and figure out what you can use for your work on mobility data.
12:01
Now to the realization, so as I mentioned we had a lot of discussions in the group, in the project group, which is a very complementary group of stakeholders, so the 10 partners, we get to our reference group for the implementations, so we did some planning, it was quite agile and iterative process after the project did start. Based on the planning
12:26
we did the first implementations, we are doing continuously iterate on that, so it's planned in a very agile way to get further feedback during the three years project into the system that we can react on the feedback and implement this. And overall I hope after three years we are
12:46
departing and are all happy to provide some proper solutions for these challenges. So that's the big vision. The architecture how we will realize that, you see here on the left hand on the number one there are the actors that are the data consumers, we have
13:05
data channels that are providing data via API services, we then have components that we want to normalize in the project to have like a kind of standardized access on the data. I will further explain how this is needed. We are having a metadata catalog available,
13:28
one or more, you heard about GeoNetwork but there are also more like Open Data Soft or Seacon and also further ones. We are figuring out how to register services to make them compliant
13:41
to this and then make this data accessible for the data processes, for the anonymization, for data fusion, data enrichment to make it usable also in our living labs as a first start. So I mentioned standards, standards in geo data I think most of us are pretty much aware of that.
14:05
So I think the OGC standards are quite common, everybody knows about WMS, WFS, sensor observation service, the new OGC API features but also OpenStreetMap data, so the SOTM was right a couple of days ago. Then the proprietary parts, the file geo database
14:26
and shape, spatial metadata has been mentioned in the talks before the catalog service for the web but there is also this DCAT standard and DCAT AP for open data at all. So besides geo data there
14:44
is also other data existing without location and they are quite popular in this sector. Besides that we are having here public transport data, trans model, NetX, GTFS, CRA, smart city data which is important, ticketing data, this OSPT, Calypso, road traffic data. So
15:08
I'm coming from here, Datix2 is quite an important standard. For new mobility data, MDS, GBFS, just other standards. Then for the implementation our architecture,
15:25
data on the web, REST interfaces are important, XML for data sharing between web services, JSON as well, the open API standard for documentation. We have cloud storages available
15:42
to name Amazon S3 which is quite popular, standards for data exchange which is quite important, standards for the semantic web. So this is just a part of standards that you need to interact with in this mobility sector. So they are much more and to get all these use
16:01
cases in such a proof of concept and such a prototype it needs a lot of work, it needs to be managed and the stakeholders need to be aware of that. So we are trying to figure out on how to narrow this scope, how to connect these standards, how to have these proper discussions,
16:22
connecting mobility services with geo data people to figure out how to connect these standards that services are possible to run on these standards. A practical example here, data. When we started the project we had access to data. So you see
16:45
here on the right hand we collected data and the first thing is, well, when you don't know what to do you take an Excel table and put like the references in. That was the first step, collecting that from all the stakeholders who had a list of a couple of hundred data sources.
17:03
Well, I'm a bit lazy, I don't want to do that and I want to search this data and yeah, we need a metadata management tool. So we did an importer for this Excel table, first thing introduced that and people saw okay well that's quite cool, I can search now these services,
17:20
metadata is available. That's not like this very boring topic but there is a use of metadata, you can find the data that you're looking for. So okay well that makes sense but now go back and maintain your metadata that others can access this metadata. It was the first part on data and I think
17:42
with data we are doing a good job, there are a lot of things possible, but service APIs are another part here and it's far harder. So the service APIs are more heterogeneous, we are having
18:03
a routing pilot at the OGC. You see here the WeGo app from here, proprietary system, we have the open route service, the graph hopper, all of them having different standards, different start destination things. So you need to bring them into one normalized schema to
18:24
make use of them and make them exchangeable. So the data analytics use case is as I mentioned you have here an example of geo network that has been shown before. The data is searchable, not just in an excel table, you can access this via standardized interface and the most important
18:42
thing is that you're using then a web service here that you can access and not just a connection to a zip file on an HTTP server which is definitely breaking the seamless user experience. You can integrate the data then in your systems, for example here in QGIS,
19:02
it can be very seamless when you're integrating the web services, you can reuse the data in further projects and it might be fairly easy. Okay, conclusion so far. So as mentioned, the mobility data is manifold from many perspectives, there are a lot of stakeholders that are working in this sector, therefore there are a lot of
19:22
standards available. We do have use cases set up with real world actors which is complicated, but it gives us the chance to get a lot of feedback here and implement the right things. We are having discussions in panels, presentations across mobility stakeholder events,
19:42
for example in the Dachshund seminar series with a lot of researchers that are working in this sector and we are involved in standards and standard discussions. We will have the transport cloud implementation talking about data services and catalogs and we want to bring this next year into these living labs with evolved use cases so far. Next, as mentioned,
20:06
the transport cloud will become into action, we will have the virtual labs and the living labs. Take a look at mobidatalab.eu and we are introducing the feedback loops and sharing data,
20:22
sharing information and sharing value. One word about opportunities, so I'm here from a company, so you have the chance to apply for jobs at the job board here in front of the venue, but also for this project, please scan this QR code or contact me,
20:45
also for further opportunities. With that, I will say thank you for your time, I hope you enjoyed listening and looking forward to questions.
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