Why create Modality?

We strongly believe that anybody should be able to understand and use transport data — not just technical experts.

9 min readJan 13, 2022


Time to reach a plot from a selected address (isochron map) with Modality.

Mobility touches everyone and it’s also key to fighting today’s climate and social challenges. We strongly believe that anybody should be able to understand and use transport data — not just technical experts. Our goal with Modality is to leverage our experience in the sphere of human-data interfaces to improve the design and coordination of sustainable mobility projects, with a focus on public transport and soft mobility (walking and cycling). Our key objective is to make well-coordinated and inter-connected public mobility networks more attractive than cars in both the city and the countryside.

Perhaps you have a train to catch and don’t have time to read everything now? Try Modality for yourself at demo.with-modality.com.

What is Modality?

Modality consolidates all of the sustainable mobility options in an area into a single, global offer, providing intuitive analysis and powerful insights.

It allows you to easily understand and visualise multimodality, which is the choice of transport available for journeys, as well as intermodality, or the effectiveness of combining several modes of transport for journeys. The tool allows you to conduct easy and fast simulations of different scenarios so that their impact on residents can be quickly assessed.

The tool has been carefully designed and developed to ensure that the most relevant data makes it into the hands of all stakeholders involved in mobility projects — especially those without a technical background. It transforms data into meaningful information and allows you to experiment with different variables so that you can have a better understanding of how a complex system like a transport network actually works. With Modality, we want to empower all stakeholders — transport authorities and local government, as well as transport operators and providers. They can then make well-informed decisions and create solutions based on their own understanding of the data rather than relying on data experts.

Modality works with open data to provide easy-to-understand overviews of transport options and the socioeconomic demographics of an area. It can be further enhanced with proprietary data that describes facilities or passenger flows. For example, a local authority may choose to add GIS data that shows future cycle paths, or an operator might want to include OD (Origin-Destination) information from ticket data. Modality is based on interactive mapping and data visualisation technology components that are driven by a powerful calculation and simulation engine.

Who’s behind Modality?

Modality is the result of R&D work undertaken by Dataveyes, a studio that specialises in human-data interaction.

Nicolas, Léo and Caroline created Modality within Dataveyes, a company specializing in human-data interactions. Photo credit: Benoît Lucet.

Here at Dataveyes, we’ve been building innovative and interactive visual solutions for over 10 years — all driven by a desire to make data easier to understand and use. Now, we want to use our experience to help a cause close to our hearts: the development of transport networks that meet today’s environmental and social challenges.

In 2021, transport was the leading sector in terms of greenhouse gas emissions in France, accounting for 30% of total emissions. In addition, 7 out of 10 people in the country travel to work by car. Paying for transport was also the main expense for the average French family, with 1 out of 4 people in the country having to turn down a job offer due to a lack of accessible transport options. Offering clean and economical solutions for daily transport is not only vital to help address the climate emergency, it’s also a key element in achieving social equality.

At Dataveyes, we have a wealth of experience working for energy and transport companies both in France and across Europe. This has allowed us to test the potential of data in the real world. It has allowed us to see and test for ourselves the positive impact that data can have on the design of mobility networks so that they can meet the challenges of tomorrow’s cities.

Even so, over the years we have also witnessed a lack of understanding of data and an inability to fully exploit it as part of the decision-making process. Modality was born from a desire to remove these barriers. We want to give city planners and public transport operators the same data agility as their digital competitors. We want to offer public transport authorities and other local decision-makers the same ability to benefit from data as Google and Uber.

Which issues are we addressing?

Our years of experience working with different players in the public transport sector, combined with our own in-depth interviews and research, have highlighted a key problem affecting mobility decision-makers. They do not have a comprehensive overview of different transport networks and how they work together. At the same time, they do not understand the impact of these networks on an area and its residents. Often, they are too focused on operations rather than grassroots outcomes.

Data analysis operations that may on the surface appear simple are often difficult to implement. For example, a key to making informed decisions is an ability to visualise all available modes of transport in an area, while at the same time identifying underserved populations and pinpointing common routes where taking a car is the only option.

Instead of offering stakeholders a common vision and helping them to work together, data today often remains partitioned by activity and mode of transport. It’s used to try and optimise solutions that are already in place or to improve existing day-to-day operations. It’s not used to design the networks of tomorrow.

Until now, a tool did not exist to model scenarios across a complete area and provide a comprehensive overview of the accessibility of trains, buses, bicycles and on-demand mobility. This lack of functionality has direct consequences on the attractiveness of alternatives to the car, resulting in poor public transport journey connections, as well as a lack of coordination between trains and buses. It has led to passenger delays at stations and journeys that are too long or too fragmented, as well as facilities that are either too busy or severely underutilised.

“The NOTRe law transferred responsibility for transport in France from the departments to the regions, but officials don’t have the tools they need to deal with all bus and rail transport networks, as well as the ports. There are opportunities for better network connections, but they don’t have the necessary tools to make it happen.”

Regional GIS manager — interviewed in November 2021.

More and more data is becoming available to help analyse mobility networks and show their effectiveness. Why is this data not being used more effectively in the decision-making process?

The difficulty in gathering quality data is often given as one reason. The variability of data, the complexity of formats and the difficulty in securing mature data sources can make it hard to maintain a functional and up-to-date database that contains all the data needed to understand the system in its entirety.

“Often data is misunderstood and misused because it comes from different sources.”

Division manager at a transport operator — interviewed in November 2021.

The specialised and technical nature of the tools required is another reason often cited. Mobility data is considered too voluminous or too specific to be processed with commonly available tools such as Excel, BI (business information) tools or generic GIS tools. Only a handful of professionals already specialising in data have the appropriate tools, which are complex to use and usually the domain of experts. They are also often segregated by transport mode.

“When an operator responds to requests that relate to trains, they do not consider the bus — they only talk about trains. We don’t know how many people are impacted by changes. Cities and regions do not consult with each other.”

Technical studies officer at a transport operator — interviewed in November 2021.

A third explanation is a lack of internal expertise. Often the analysis of advanced mobility scenarios is only entrusted to specialised research firms or to an analyst in another department of the organisation. The latter often delivers studies in formats like PDFs or even paper — neither of which is interactive. These formats can’t respond to variable adjustments and are remote from the actual source data. Challenging a hypothesis or testing a new idea is therefore often discouraged because it means restarting the analysis — and paying again — in time or money.

“Using GIS, operators can take a week or so to produce results — and their output is neither dynamic nor interactive. The cumbersome nature of GIS tools does not allow simple iteration of the results and each data request is costly. It’s important to know in advance what you want and, in the end, you just end up with 5 or 6 static images.”

Mobility data specialist — interviewed in June 2021.

France’s 2019 Mobility Orientation Law (LOM) states that all data related to mobility, whether theoretical or real-time, should be made public. Even so, main stakeholders involved in the mobility sector, including public transport authorities, local government and operators, are missing out on access to this data — and losing the potential to radically improve their strategic planning.

What will change thanks to Modality?

With Modality, we are leveraging several themes to ensure data can be used as widely as possible.

— Data visualisation. Modality uses data visualisation, interactivity and explorable explanations to translate mobility data into meaningful information. The user-centric design of the tool allows non-technical and non-expert users to take ownership of the data, to play with it and to understand it without the need for external assistance. It puts the data in the hands of the decision-makers, ending their dependence on experts.

Case Study “Grand Paris 2021 vs 2030” — Modality

— A multimodal approach. Modality allows you to easily visualise all transport options in an area so that you can analyse them as a single global offer. It highlights connection and coordination issues between transport options that may have otherwise remained hidden. It acts as a multimodal “mobility observatory”, centralising data from the various modes of transport and facilitating better interconnectivity.

Case Study “Grand Paris 2021 vs 2030” — Modality

— Citizen-centred indicators. In addition to network-centric indicators, such as kilometres travelled or occupancy rates, Modality also provides indicators that focus on residents and the attractiveness of an area. It provides insights into the quality of access to facilities and services, the quality of nearby transport services, and walking time to reach the network, among other variables. The tool allows you to see what’s really behind the numbers, to step into the shoes of the elderly or people with reduced mobility, to define maximum journey times, to study the socioeconomic demographics of the population and lots more.

Case Study “Grand Paris 2021 vs 2030” — Modality

— Rapid generation of detailed analyses. Studying the impact of mobility options in an area requires calculations that are often long or expensive to conduct. For example, the access time from each point in an area to every other point. Modality is based on a powerful route calculator that has been specifically adapted to generate impact studies. It can produce detailed low-level studies in just a few minutes.

Case Study “Grand Paris 2021 vs 2030” — Modality

— Scenario simulation. Modality allows you to create mobility scenarios and instantly simulate their impact on residents. The tool makes it easy to adjust data such as bus routes, timetables and bike stand locations, but it also allows you to add or remove entire elements. This makes it possible to rapidly test new ideas or solutions and to make “live” iterations with the data without having to wait for a technical expert. This can help inform meetings quickly and dramatically change the way decisions are made.

Case Study “Grand Paris 2021 vs 2030” — Modality

— Reusability of studies. Modality offers turnkey analyses that are quick to set up. The tool allows you to capitalise on the studies that have already been carried out. Each time a dataset is uploaded or each time an analysis is baselined, they are available to all users. This has impressive cost implications — the cost of reuse is zero, the cost of accessing an additional study is zero. This starts a virtuous circle of data use: the data is used more thanks to the tool, so it becomes more interesting to import more data. As a result, the data is used even more and the cycle continues.

The best way to understand the potential impact of Modality is to try it for yourself. To help, we have produced a series of case studies that showcase the main technologies the Modality tool uses. You can access them demo.with-modality.com.




Modality helps to visualise, analyse and simulate all transportation options in an area, presenting them as a single global offer. http://with-modality.com/