Mobility within cities has been a challenge for cities and citizens alike. Data on movement can help cities better manage the traffic of vehicles and pedestrians, especially during peak times of congestion. With a digital layer of the city, deep insights on live movement data can be collected from both citizens and sensors from the environment to better understand, manage, model and regulate traffic flow.
Data Capture Methods
Support better urban design, safety and planning requirements
Realtime traffic management & even public transit dispatch based on traffic flow
Innovative mobility service offerings that address market gaps
Improve safety through improved enforcement (red lights, speeding etc.)
Faster provision of emergency services
Travelling through a city we generate several types of data that through transportation infrastructure, and we also generate personal data. Understanding the citizen's journey through the city as well as how their journey compares to the aggregated population can provide great insights. This data can be collected through current devices and applications on phones, such as directions in maps, upcoming appointments in calendar, and movement data through GPS. Traffic flow and management systems can capture data through sensors to track road utilization, types of vehicles (ie. transit, biking, scooters, walking). Adding in weather and environmental data collected through sensors will add another dimension to understand how people move through the city under various weather conditions.
This information will be valuable to many organizations. Major mobility players, including local and regional mobility agencies, mobility technology players and platforms, transportation planners and urban planners to better manage traffic and congestion in the city. Other users of this data include emergency services, venture companies, and navigation platforms.
The civic data trust will need to incorporate the views of many stakeholders for the benefit of moving people more effectively and keeping pedestrians safe. A non-profit organization would be in a position to keep citizens best interests top of mind. Decision making ability will consist of a collaborative jury, including representation from citizens, government, private corporations, and academics. The enforcement mechanism would be a transparent enforcement process, where government legislation would be the standard with ability for the body to have the right to audit to ensure active compliance by organizations.
This data will be held in a semi-decentralized structure, with much of the infrastructure and public data sources held locally with public institutions. Private sector data will be stored via open cloud services. The trust will help open the data to collaboration with public agencies, the local innovation community and private institutions to foster innovation in this space.
If government data is to be incorporated into a digital trust, then government needs to be part of the decision making group around what uses that data could be used for. Government would also need to retain the right to audit what its data was being used for.
Specifically define that the 'Types of Data' includes data provided by private sector actors.
There was an initial assumption among users that the data under consideration was only public sector data. Therefore, there was an initial view that this data should be made available as open data, so that it does not give any one party monopolistic advantage in accessing the data.
Later in conversation, a need was identified to specify that the data trust would also include data from the private sector. Law enforcement uses generated a fair amount of discussion, with concerns expressed around predictive policing and policing bias, if there was now a way to tie an identity to a vehicle (based on real-time license plate detection) even before the vehicle or driver violated a rule.
There was a strong need expressed to consider data de-identification at source by default, where use case specific exceptions. For example, for law enforcement purposes data cannot be de-identified at source.
There was general consensus that given the volumes of data involved around mobility data, it would be better to have a decentralized model for data storage, which could then be pulled from as per the rules of the data trust. This would ultimately also be more secure and efficient in terms of data management.
Standardization and indexing of data will be critical to the usability of the data given the different types of providers of the data, both across the public and the private sector data holders
Given the various scale of geographies at which mobility is undertaken, there was also a view that the answer to what and how mobility data is managed within a trust would be different. For example, the answer to what and how mobility data is shared within my community will likely not be the same as a national scope mobility trust.