Mobilletic: big data to improve public transport
Between 2013 and 2016, the Mobilletic research project scrutinised the Korrigo Pass’s ticketing data in Rennes; an experience which highlighted the role of big data in travel research and the optimisation of the transport offer. Here’s an interview with Latifa Oukhellou, Project Leader and Research Director at the French Institute of Science and Technology for Transport, Development and Networks (IFSTTAR).
How did the Mobilletic Project come about?
The question at the outset of the Mobilletic Project was as follows: to what extent can digital traces collected by ticketing systems, traditionally used for pricing and to combat fraud, be used for other purposes and, notably, to analyse users’ journeys?
Conducted in partnership with the Ecole des Ponts Paris Tech, the CEREMA, the public transport operator Keolis and the Metropolis of Rennes, our research has mobilised a multidisciplinary team –data, development and geography specialists etc. – to explore three topics:
- what are the travel habits/the homogeneous groups of users?
- what are the bus-metro intermodality practices in the city of Rennes?
- what is the impact of projects and public transport policies? (taking the example of staggering lesson start times at the University of Rennes 2)
What have you discovered with regard to travel habits?
Aside from regular travellers, who primarily use transport early in the morning and in the afternoon, a large proportion of users (60%) have less clear-cut travel habits, such as pensioners who use transport mid-morning and young people who use transport in the evening. Data mining enables the notion of the ‘average user’ to be moved away from in order to develop a dynamic vision, in space and time, of user activity. This continuous description can help urban managers and operators to better organise transport.
Your research also attests the validity of certain public policies…
To address the increasing congestion of the metro line (a 200% occupancy rate at certain stations), in 2012, the City of Rennes and Keolis embarked on an experiment which involved staggering lesson start times at the University of Rennes 2 by 15 minutes for half of its students. Our research revealed that, without addressing global transport congestion, these staggered start times enabled the demand for transport to be better distributed, vehicle occupancy to be reduced and the quality of service to be enhanced.
Big data from ticketing systems also enables knowledge on intermodality to be improved
Ticketing data from the Metropolis of Rennes’ bus-metro network is an effective means of developing knowledge on daily intermodality. Interchange hubs can be analysed in detail and users’ movements can be accurately observed during their transport connections. This is valuable for connecting different modes of transport.
In your opinion, what does ticketing data analysis offer in comparison with traditional travel surveys ?
Traditional mobility surveys are valuable: they cover all transport, provide information on travel motives and contain socio-professional data, unlike ticketing data which is rendered anonymous to respect users’ privacy. On the other hand, these surveys are expensive, infrequent and do not allow a close monitoring of developments in mobility. Thanks to ticketing data, we are able to benefit from a detailed observation of mobility in space and time, and assess the impact of transport measures (pricing, connections etc.) on mobility behaviour. These two types of analyses are therefore complementary.
Which prospects have been opened up by ticketing data analysis?
There are many prospects, notably thanks to the interplay of other data (survey and smartphone data etc.). Today, a metro station merely displays when the next train will arrive but tomorrow, it will also be possible to benefit from comfort and carriage occupancy indicators etc. The analysis of this data will also enable operators to offer a smooth, efficient service by making more trains available and changing the size of buses to reflect demand in real time.
The benefit of ticketing data is therefore threefold: it enables users to better plan their journeys and to enjoy a better quality of service, transport operators to better understand passenger flows to anticipate needs and predict demand, and organising authorities to optimise the transport offer and adapt the level of service.
Latifa Oukhellou is Project Leader and Research Director at the French Institute of Science and Technology for Transport, Development and Networks (IFSTTAR).
Photo credits: D.Gouray
Category : Research and innovation /
06/01/2017
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