Big data: a mobility case

‘Mobiliteit vanuit-de-Mens’, a strong basis for a variety of client situations
Every time a person leaves home and moves around this is a personal decision. Hence, looking at mobility from the perspective of the individual makes sense. Large scale transportation and mobility can be understood by viewing it as the sum of many individual decisions. Developing a view on individual options for mobility, actual choices and motives provides a strong basis for understanding drivers of actual choices. We have systematically built such a broad insight of the mobility behaviour of Dutch citizens, by combining various public sources in a knowledge base that we call ‘Mobiliteit vanuit-de-Mens (MvdM)’. This knowledge base proves very useful for a variety of client situations.

Client example 1: designing future products by assessing market potential
A prominent transportation company has the chance and challenge to make an offer for its future product. Where can they compete with car transportation? And with competitors in the sector? What offering will attract most travellers? Is it about travel time, comfort, stop density? To crack this case, we combined 1) our own multi-year insight from ‘Mobiliteit vanuit-de-Mens’ as a basis to formulate hypotheses and 2) a substantial amount of anonymized check-in/check-out data to test these hypotheses. We determined no regret minimum options and a road map of more challenging add-ons based on empirical results. Several years later, the success of the proposed service lines can be measured in real-life.

Client example 2: spreading peak demand in public transport
Public transport demand is strongly peaked, especially in the morning with a ‘hyper-peak’ of 45 minutes. Evidently, this leads to high operational costs and/ or capacity issues. On the motorway, congestion and loss of time cause drivers to shift their travel, widening the peak to 2-3 hours. In public transport, crowdedness of vehicles does not have the same effect, but does lead to significant complaints. So how to solve this? Should a specific customer segment be targeted? Is cooperation with employers/ universities desirable? Can a financial incentive work? And is that to be a discount, a premium or a combination?

Client example 3: parking policy for national government
Explorative study for the national government to analyse the impact of parking policy on mobility choices – available online as a project delivered under the SDP brand. A strong correlation is shown between restrictive parking policy and the market share of cars especially in the ‘G4’ (four biggest municipalities/ ‘gemeenten') – stronger than traditional factors such as congestion, public transport quality and concentration. Relevant policy questions are raised: how should the G5-G10 employ parking policy to support future development? Can increased differentiation of parking tariffs be used to further spread car traffic? Can parking policy be employed to relieve ‘expensive’ bottlenecks in the current infrastructure?