We always believed Smart cities could improve the future life of their citizens, now cities must become smart overnight.
Dramatic changes in mobility policies necessary to contain the virus spread while allowing people to work and live:
-public transportation won’t be able to “carry enough people safely”
-bikes are safer and grow in numbers
-car traffic will “change”
Coupled with unprecedented speed in decision making required to launch, monitor effectiveness and eventually change course of the implemented policies.
A flexible and powerful data platform that allows the public administration to understand the evolution of urban mobility, its new needs and trends and promptly act on them.
Our final objective is to empower the public administration to ethically guide/drive citizens behaviour for the good of the community.
What it does
What are the KPIs we wanna measure: when and where
“What we have”: mobility patterns from a sample of connected cars, contextual data (geographical, street info, )
“What we plan to add”: social variables (demographic), cell phone, OEM connected cars, public transport, municipality DB, police forces DB, electric scooters providers data, bikesharing providers,
-Traffic trends and visualizations: to better understand the ensuing reduction of mobility flows,
-User flow mapping: to quantitatively assess the impact of mobility restrictions and social distancing on single individuals or networks of people
-Comparative analysis: of key mobility parameters related to traffic pollution and demographic patterns
-Mobility evolution: track change in private vs hybrid vs public transportation
-Geographical access control: monitoring the impact of temporary restricted access areas and planning user flow accordingly
How we built it
In order to develop the solution, our first approach is to rely on the AWS suite through its several services provided that are already integrated in our data management platform.
The main services of the AWS Suite involved are Redshift (the DWH) - that allows to manage huge amounts of data - and Quicksight (the B.I. data visualization tool) providing a wide range of graph and visualization charts to enhance the insights gathered from the data.
The data follow this flow: after the collection from the vehicle, data is enriched with the contextual data from external data providers and then stored in the DWH where are elaborated for the ingestion in the B.I. and then presented on it
Challenges we ran into
Data access: we need data from more connected cars and integration of additional data sources as mentioned before. From the technical side, the main challenge has been the creation of the necessary pipeline needed to feed the visualization tools.
Accomplishments that we're proud of
We are most proud of the fact that we are able to develop big data solutions that can have a significant impact on local and global communities in a small team and on a tight budget and schedule.
Since the beginning of developing the solution, one of the first accomplishments gathered is that we proved to be confident in deploying solutions through different visualization tools and understand capacities and capabilities of each tool.
Another accomplishment for us is the capacity to effectively manage the huge amount of data coming from the connected cars and 3rd party sources.
What we learned
Great teams always perform at their best under pressure for example when doing hackathons.
The extent of the challenge we currently face is so large that a solution cannot fail to include a real collaboration between private companies and public organizations.
What's next for Debugging Mobility
We are looking forward to implementing a scalable solution for all European countries and working with established data providers from connected cars and on government database sources.
We look forward to finding solutions to finance the current project development and getting involved with/visible to potential stakeholders to grow this project further.