The mobility data need is growing for many actors in the ecosystem, including cities, transportation companies and consulting firms.
At La Fabrique des Mobilités Québec, we are working on this issue in our project “Our open mobility data” with the objective to have a better understanding of the new post-pandemic travel habits and the validation of the relevance of a travel data collection tool. This project, in collaboration with the Coop Carbone, is financed by the Ministère des Affaires municipales et de l’Habitation (MAMH) via its Fonds d’innovation et de rayonnement de la métropole (FIRM), as well as by the Montréal en Commun program and the Smart Cities Challenge.
Why is travel data important?
Mobility data has long been, and still is, the basis for transit planning. The organization of an efficient transportation system requires a modeling phase, during which it is important to have reliable baseline data to make projections. The limitations of the travel data collection techniques used to date suggest that there may be a need to consider how to improve the methods used to achieve better data quality and reliability.
An app to collect travel data instead of the OD survey?
We often hear that mobile applications or telephone operator data will offer more accurate and up-to-date data than traditional origin-destination (OD) surveys.Indeed, origin-destination surveys, which are currently the basis for transportation planning, have many vulnerabilities in terms of coverage, periodicity and specificity. Conducted on a weekday in the fall every five years, the OD survey will not provide travel information on weekends or in other seasons. Moreover, in 5 years many changes can occur.
But if it is for the moment the only source of reliable data, it is because of the robustness of the sampling techniques developed so far, in order to offer representative samples of the surveyed population, which can then be adjusted and extrapolated to draw a mobility portrait of the whole region’s population.
On their side, mobile applications can have quite satisfactory results as far as data quality is concerned, but there are many shadow areas on these tools. We will start with the sample, if we manage to obtain a representative sample with the OD surveys, we cannot say the same for the applications. To rely solely on this type of tool for planning would mean neglecting certain categories of the population, those who do not have a smartphone, or those who are not familiar with technological tools.
Beyond the challenge of having users representative of the population, there are also technological challenges, since applications can be quite powerful in detecting the modes of travel used during a trip (based on the speed of travel and by querying a public transport database). Thus walking, driving, bus, metro, train or bike can be detected, but for a trip by cab, it will be difficult to know, since it will be considered as a car trip. And we can also give the example of the electric car which cannot be differentiated from a conventional car.
If we say that the modes can be detected with an accuracy of 75% in the best cases, what about the purpose of the trip?
Without the user’s intervention in the application to validate or fill in the purpose of the trip, this information cannot be known by the application itself, knowing that the purpose represents a very important parameter in the planning studies of transportation systems. So to say that mobile applications will replace OD surveys, we are still far from it. Rather, they will be complementary methods to have more precision and a better understanding of punctual mobility phenomenon.
Why people will accept to use such an application?
At La Fabrique des Mobilités Québec, we are particularly interested in this question and we want to position ourselves as a provider of answers, so we have developed our open source application Ma Mobilité, which is available for download on the stores.
Knowing that there are other players in the ecosystem who are working on other applications, our goal is not to compete with those applications, nor to make commercial use of the tool or the data collected. Rather, we want to help them position themselves to achieve their goals by providing answers as we experiment.
Our users recruitment strategy is simple: we offer an application that informs users about their mobility, from the number of trips made by mode to the number of calories burned or the carbon footprint related to travel, including the number of kilometers traveled by mode and the travel time. They are also informed of the importance of their data for the community, since this data can be used for better transportation planning by knowing their needs better, preserving the environment by promoting more sustainable ways of transportation, or making them aware of the impacts of their trips to help them adopt a more eco-friendly behavior.