People’s mobility is a key decision driver in multiple industries and public agents, including transportation, tourism and retail. Traditional methods to estimate the number of visitors, traffic flow or pedestrian footfall, are based on a one-off, difficult to scale face-to-face interviews or other manual methods such as car counting stations. Some inefficiencies of these approaches (limited sample, continuous tracking, biased, cost) create barriers data availability and insight to understand the mobility of people in different environments of interest.
Kido Dynamics aims to democratize Big Data, with one of the most advanced insights to boost the data-driven economy. We leverage mobile phone data, the most accurate proxy to understand people’s mobility.
With our technology, companies, governments, and public institutions will have available powerful tools to make the right decisions and make it faster, smarter, and better informed.
Private and public transportation is a major concern in all developed and developing cities. Cities suffer from massive traffic jams that have a direct economic impact in job hours lost, without considering collateral impacts on health, security, or pollution to name a few. DEMOGRAPHICA can contribute to optimize traffic flows and make more sustainable cities worldwide.
In the tourism sector, DEMGRAPHICA improves demand KPIs, characterizing visitors both spatially and temporally, which allows high selectivity in the analysis of areas at risk of saturation tourism. It can help the destination to be much more responsive and agile regarding existing needs and future challenges of their transportation network, tourism industry, and public resources management in general.
It is useful too in the COVID scenario because mobility has been drastically altered by the COVID crisis and confinement, teleworking, and mobility restrictions have had multiple impacts on it.
DEMOGRAPHICA project´s main objectives are:
• Successful development of an algorithm to analyze collective human behaviour and produce per-location, per timeframe, actionable and reliable predictions fully customized as per each client’s vertical needs.
• Make use of socio-thermodynamics to analyse the complex, massive and anonymised data provided by Mobile Network Operators (MNOs) to forecast mobility patterns of the general population.
• This allows us to understand and reliably forecast citizens’ mobility patterns without violating their privacy.
