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Reporting period: 2019-09-01 to 2020-08-31

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. DEMGRAPHICA 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.
Tasks implemented M01-M12:
The Kido team has addressed the main technology challenges of the project. Firstly, we have defined user cases, identified functional and operational requirements and defined the final architecture in a private cloud AWS, based on serverless applications. We have contained the logical pipeline into a Docker image (replacing the initial idea of ISO image) to simplify the installation process in MNO infrastructure. Quality of service is ensured by an agile continuous integration infrastructure.
Furthermore, in the same period, we have developed an algorithm that reconstructs the trajectory of a mobile user through ML and conceptualized the essential KPIs in transportation projects: trip, micro trip, macro trip and stay. It is complemented with a routing feature to accelerate the internal decision-making process based on the analysis of the data.
Regarding the availability of MVPs and early adopters, the initial Workplan has been an improvement because we have carried out a publication platform managed in a specific infrastructure to securely extract results and can be shared with clients through web access and API. Additionally, we have deployed the graphical user interface (GUI) and integration with the process layer in Transportation, Tourism and OOH-advertisement applications.
Main results:
From the infrastructure point of view, we have integrated KIDO’s system into the architecture of different operators and validated the management and integrity of the metadata (Orange Spain, Claro Brazil, Oredoo Qatar and Kuwait) and finished the analytical procedures to obtain useful insights for transportation by introducing simulation schemes based on ML.
On the commercial side, we have successfully solved different transportation uses case and annual subscription by early adopters TEMA GC and CPS have enabled the refinement of the platform architecture and its validation in the load scenario.
Additionally, we have carried out PoC to evaluate interactive GIS platform user experience with Valladolid CC, Banco de Santander and iWall company. In the first case, we have integrated and validated specific transportation features taken advantage of new project requirements: modal and light/heavy vehicles segmentation, traffic flows in checkpoints predefined in the main road of Valladolid city. The project for iWall focuses on OOH advertisement.
We have complemented with MVPs for tourism availabilities in and
The main improvement has been our “data wrangling” patented solution for the reconstruction of full individuals’ trajectories based on physic inspired methods. This allows us modeling population using physics and obtaining the most probable outcome for a given configuration.
Our competitors instead use simple origin-destination matrixes which gives a much lower level of detail on people’s mobility. It can be easily implemented in any server worldwide because it operates through an API and security and privacy are guaranteed by design.
Expected results:
We have based on cloud AWS our concept of mobility analytics as a service and have deployed and standardized the DevOps process of MVP-transportation upon agreed early adopter’s metrics. It has been validated through PoC with strategic clients/prospects. Moreover, we have developed a final version of MVP for Tourism and Retail/OOH.
Results can be shared with clients through two approaches: by specific query on-demand through a secure web access environment and automatic massive download of results by the configuration of a proprietary REST API defined for each project with Token authentication
KIDO breaks complex data sets to help organizations understand human mobility in a simple way to make smarter decisions. KIDO joined RDTI of Spain in November 2019 and presented a fully automated solution that allows a continuous update of metrics and KPIs. 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.
KIDO DYNAMICS analysis on people's mobility and its correlation to COVID-19 spread is still the main reference to explain pandemic incidence in Spain. Our project for MITC to provide the main people mobility KPIs for seven main Spanish cities has allowed public decision-makers to understand what the impacts of political measures were taken to reduce the spread of the pandemic in Spain. In the same way, our collaboration with IFISC of CSIC Spain in a research study focus on the effects of mobility and multi-seeding on the propagation of the COVID-19 in Spain has had a significant social and political impact.