By David O’Neill, Director, Kisio Analysis and Consulting
The Business of Mobility is an Urban Mobility Company series highlighting some of the most successful new businesses in the mobility sector. Featuring a closer look at the way in which companies stand out, CEOs, Directors and other c-level executives elaborate on what it takes to turn a great idea into a great company.
David O’Neill of Kisio Consulting & Analysis explains how data-collection is revolutionising urban mobility and helping to lay the foundations of mobility management platforms for cities and transport authorities across France.
I have spent much of my career in the transport sector and was engaged in numerous mobility projects for EY, and then for the Île-de-France Mobilités, before joining Keolis around three years ago. I’ve seen rapid changes in the industry, particularly when it comes to data collection which – in some regions – still relies on survey questionnaires.
In early 2020 I had the opportunity to head up Kisio Consulting & Analysis (a Keolis subsidiary) and launch a mobility data-analysis offer to the market. There was immediate uptake as we launched numerous projects for government and transport authorities across France.
Using GPS and Wi-Fi to generate a precise view of mobility
Our mobility data is collected in two ways, GPS and Wi-Fi, both of which rely on smartphone signal. GPS tracks movement on a large scale, whereas Wi-Fi helps give you a read on discrete locales, e.g. malls, stations and airports. For GPS we only need track around 5% of a target demographic. With this sample we can cross-reference against census data and calibrate accordingly.
Thanks to sophisticated algorithms (our ‘secret sauce’) we extrapolate the information to create an accurate picture of mobility trends and habits. And when we combine the two different data flows from GPS and Wi-Fi we get a clear view of mobility in cities and across regions: a system that’s workable for just about any country in the world.
It’s fascinating: we can see where people live, where they work, their regular habits, whether they have a second home, and whether they work in the city and live elsewhere. In other words we can generate a precise view of how people move.
Using data to respond to COVID-19
Given the timing, several of our projects were related to the COVID pandemic. In the wake of the first lockdown in May, the French Minister of Transport was concerned that when lockdown ended traffic jams would skyrocket – as had happened in Wuhan. The other fear was that people travelling by public transport could spread the virus. We had 15 days to build the tools to analyse mobility in eight major urban centres across France. Every day we presented the stats to the minister for him to track trends. But our work is not only to help government officials make decisions. In another project we used Wi-Fi signal to predict occupancy levels in busses. This information was sent to commuters – via smartphone – so they could wait for an emptier bus and limit their chance of catching COVID.
Mobility management platforms
The thirty studies we did were a great success. We showed clients that in a matter of days we could give them clear and precise answers about mobility patterns, e.g. density of pedestrians on certain streets; the destination of train passengers on reaching their final stop; shifting trends in car-use versus train use for city commute, etc. Delivering direct answers to straight-forward questions drove the first stage of our growth.
The second stage of our growth is about helping cities anticipate trends and demand. This involves building software tools with predictive algorithms that tell the client how – for example – a public event, or rainy weather, will affect mobility patterns.
This sort of predictive dashboard is what some are calling a mobility management platform. Smart data and machine learning will play their part in creating sophisticated solutions for authorities to manage mass mobility to the benefit of commuters and the city at large.
MaaS: when are we going to get there?
It’s exciting seeing so much interest and growth in a short space of time, but there is much refinement still to be achieved. Currently our systems cannot quite distinguish a scooter from a bike, or e-bike from bike, something we are working on. The science of data gathering and management will become ever more sophisticated, giving MaaS providers a clear view of demand and then matching the offer to the demand.
Cities will increasingly look to data-driven platforms to help them improve traffic flows, reduce cars and build the foundations of MaaS. It’s exciting to be on the cutting edge of the mobility revolution as the Internet of Things (IoT) edges us ever closer to the dream of mass mobility as a service.
Continue the discussion with David at Autonomy Digital 2.0 (19 – 20 May) where Kisio will be an Industry Partner. Learn more about the world’s largest virtual urban mobility event.