The SIGNALEC case study, from the City of Montreal, is focused on a shared system used by a number of Montreal boroughs to pool and manage their signal data inventory more efficiently. The SIGNALEC system is a concrete example of urban mobility data management, from its collection to its use to improve parking and traffic management in the city. In this article, we will illustrate the application of various concepts linked to the data journey, through a concrete use case from the SIGNALEC project.
Contextualizing the SIGNALEC project use case
SIGNALEC data, a dataset on on-street parking signs in Montreal, is available on the City of Montreal’s open data site. This data is not regularly updated by the City or the districts, which is why the use case analysis was crucial in order to understand the process of validating the usability of the data and thus enable its valorization. This information is useful for a variety of parties, including regulatory authorities, mobility companies, application developers and the general public.
La Fabrique des Mobilités Québec used the SIGNALEC case study to illustrate the application of its data journey mapping tool. This use case shows the different stages in the data journey, from creation to use, via the various stages of processing, analysis and diffusion. Some of the problems with the data journey occurring within the SIGNALEC use case have motivated this initiative to help identify and make the parties involved realize their risks and stakes, their opportunities and their roles. The exercise was certainly able to perenialize the process of valorization of these data by the ecosystem of interest.
Graphic presentation and explanation of use case
In this section, we will illustrate the application of the Fabrique des Mobilités Québec mapping tool through the SIGNALEC use case. This use case illustrates the various stages in the data journey, from its creation to its final use. It highlights the crucial role of the various players involved, their respective responsibilities, and the risks and opportunities inherent in each stage. The objective here is to make tangible the importance of proper data management in urban mobility projects, and to show how our tool can help navigate this complex process effectively. So, let’s dive into the SIGNALEC case study.
Risks and challenges:
Data management presents a variety of challenges for each player. For the data creator, it’s a question of balancing user demands with internal capabilities. The data-sharing manager must ensure that information is up-to-date, properly secured and usable. Users, for their part, depend on reliable data to derive value from it. Last but not least, the valuator must ensure that the data is kept up to date and of real value to the end-user.
Despite these challenges, there are significant opportunities. The data creator can benefit from data enhancement without investing resources of his own. The data-sharer has the opportunity to offer a valuable service to its citizens, and facilitate the deployment of sustainable mobility solutions. Users and customers gain access to a new service that can optimize their mobility. Last but not least, the developer has the opportunity to make the data useful in promoting more sustainable mobility in the region.
Each player has specific responsibilities to ensure the correct flow of data. The data creator is responsible for entering information into SIGNALEC. The Data Sharing Manager plays a crucial role in data extraction, analysis, preparation and publication. Users and beneficiaries consume the valorized data. Finally, the valuator is responsible for extracting the data, converting it to the right format and visualizing it on the OpenStreetMap.
In conclusion, SIGNALEC’s data journey illustrates the challenges and opportunities associated with data management in the context of urban mobility. It highlights the need for close collaboration between the various parties involved to ensure data quality, security and value.
The application of these tools to the SIGNALEC case provided a concrete illustration of their utility. It became clear that data management involves many players, each with specific responsibilities, facing particular risks and benefiting from different opportunities. Mapping these elements gives us a clearer picture of the data ecosystem, and enables us to identify levers for improvement.
In the future, it will be interesting to see how these tools evolve and are adopted by other organizations with the support of the Fabrique des Mobilités Québec. The ultimate goal is to democratize the use of data and make it truly useful for the deployment of new forms of mobility. There’s still a long way to go, but the first steps are promising!
Article written by Jeremy Laplante